How can we apply machine learning techniques on graphs to obtain predictions in a variety of domains? Know more from Sami Abu-El-Haija, an AI Scientist with experience from both industry (Google Research) and academia (University of Southern California).
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
We present Graph Convolutional Networks that, unlike classic DL models, allow supervised learning by exploiting both the single node features and its relationships with the others within the network.
The document discusses subspace indexing on Grassmannian manifolds for large scale visual identification. It proposes using local subspace models built on neighborhoods defined by queries, but notes issues with computational complexity and lack of optimality. It then introduces Grassmannian and Stiefel manifolds to characterize subspace similarity and define distances. A model hierarchical tree is proposed to index subspaces through iterative merging based on distances on the Grassmannian manifold.
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
This talk by Lucas Theis from Twitter/Magic Pony on "Compressing Images with Neural Networks" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
We present Graph Convolutional Networks that, unlike classic DL models, allow supervised learning by exploiting both the single node features and its relationships with the others within the network.
The document discusses subspace indexing on Grassmannian manifolds for large scale visual identification. It proposes using local subspace models built on neighborhoods defined by queries, but notes issues with computational complexity and lack of optimality. It then introduces Grassmannian and Stiefel manifolds to characterize subspace similarity and define distances. A model hierarchical tree is proposed to index subspaces through iterative merging based on distances on the Grassmannian manifold.
Lucas Theis - Compressing Images with Neural Networks - Creative AI meetupLuba Elliott
This talk by Lucas Theis from Twitter/Magic Pony on "Compressing Images with Neural Networks" was presented at the Learning Image Representations event on 30th August at Twitter as part of the Creative AI meetup.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris
This document proposes methods to incorporate higher-order graph properties into graph neural networks (GNNs). It shows that GNNs are as powerful as the 1-dimensional Weisfeiler-Lehman graph isomorphism test for distinguishing graphs, but cannot capture higher-order properties like triangle counts. The document introduces k-dimensional GNNs and hierarchical k-GNNs to learn representations of subgraphs. Experimental results show these methods improve over 1-GNN baselines on graph classification and regression tasks.
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs Christopher Morris
This document proposes a new graph kernel called the glocalized Weisfeiler-Lehman graph kernel. It extends the classic Weisfeiler-Lehman graph kernel to consider both local and global graph properties. The kernel maps graphs to feature vectors based on the k-dimensional Weisfeiler-Lehman algorithm. Approximation algorithms using adaptive sampling are introduced to make the kernel scalable to large graphs. Experimental results on graph classification benchmarks demonstrate the kernel achieves high accuracy while having fast running times.
We review our recent progress in the development of graph kernels. We discuss the hash graph kernel framework, which makes the computation of kernels for graphs with vertices and edges annotated with real-valued information feasible for large data sets. Moreover, we summarize our general investigation of the benefits of explicit graph feature maps in comparison to using the kernel trick. Our experimental studies on real-world data sets suggest that explicit feature maps often provide sufficient classification accuracy while being computed more efficiently. Finally, we describe how to construct valid kernels from optimal assignments to obtain new expressive graph kernels. These make use of the kernel trick to establish one-to-one correspondences. We conclude by a discussion of our results and their implication for the future development of graph kernels.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
(DL輪読)Matching Networks for One Shot LearningMasahiro Suzuki
1. Matching Networks is a neural network architecture proposed by DeepMind for one-shot learning.
2. The network learns to classify novel examples by comparing them to a small support set of examples, using an attention mechanism to focus on the most relevant support examples.
3. The network is trained using a meta-learning approach, where it learns to learn from small support sets to classify novel examples from classes not seen during training.
Joint Word and Entity Embeddings for Entity Retrieval from Knowledge GraphFedorNikolaev
The document proposes a method called KEWER that learns distributed representations of words, entities, and categories from a knowledge graph in the same embedding space. KEWER first generates random walks from entities, replaces some elements with surface forms, and then learns embeddings by maximizing the likelihood of contexts. These embeddings improve entity retrieval over term-based and existing joint embedding models, especially when combined with entity linking.
Lec-07: Feature Aggregation and Image Retrieval System [notes]
Image retrieval system performance metrics, precision, recall, true positive rate, false positive rate; Bag of Words (BoW) and VLAD aggregation.
Lec-16: Subspace/Transform Optimization
Address the non-linearity issues in appearance manifolds by having a piece-wise linear solution. Query driven local model learning, subspace indexing on Grassmann manifold, direct Newtonian method of subspace optimization on Grassmann manifold.
Convolutional networks and graph networks through kernelstuxette
This presentation discusses how convolutional kernel networks (CKNs) can be used to model sequential and graph-structured data through kernels defined over sequences and graphs. CKNs define feature maps from substructures like n-mers in sequences and paths in graphs into high-dimensional spaces, which are then approximated to obtain low-dimensional representations that can be used for prediction tasks like classification. This approach is analogous to convolutional neural networks and can be extended to multiple layers. The presentation provides examples showing CKNs achieve good performance on problems involving protein sequences and social networks.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Lec-17: Sparse Signal Processing & Applications [notes]
Sparse signal processing, recovery of sparse signal via L1 minimization. Applications including face recognition, coupled dictionary learning for image super-resolution.
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian theory and methodology in machine learning. They have achieved remarkable success in computation, and enjoy strong theoretical support. Much of the existing literature has focused on the linear Gaussian case. The purpose of the current talk is to demonstrate that the horseshoe priors are useful more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularization in nonlinear and non-Gaussian models; multivariate models; and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
This document introduces graph neural networks and discusses a claim that they are essentially low-pass filters. It provides an overview of graph neural network operations, including combining node features, aggregating information from neighbors, and updating node representations over multiple layers. The document notes that while graph neural networks may be less powerful than other deep learning methods, they are interesting for problems involving graphs, such as drug discovery and web analytics. It questions how graph neural network classifications operate and whether the low-pass filter behavior is caused by the graph Laplacian matrix.
A short presentation on the emerging research on normalizing flows. The presentations follows two recent survey papers on the topic:
[1] Kobyzev, Ivan, Simon Prince, and Marcus Brubaker. Normalizing flows: An introduction and review of current methods, T-PAMI 2020.
[2] Papamakarios, George, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference, arXiv preprint arXiv:1912.02762 (2019).
AILABS Lecture Series - Is AI The New Electricity. Topic - Deep Learning - Ev...AILABS Academy
Artificial Intelligence is transforming the world. Deep Learning, an integral part of this new Artificial Intelligence paradigm, is becoming one of the most sought after skills. Learn more about Deep Learning and its Evolution.
Multimodal Residual Networks for Visual QAJin-Hwa Kim
Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.
This document provides an overview of elliptic curve cryptography (ECC). It discusses how ECC provides stronger security than RSA with smaller key sizes. The document describes the mathematical foundations of elliptic curves over finite fields. It explains scalar multiplication, which involves adding a point on the elliptic curve to itself multiple times, as the core operation in ECC. Finally, it discusses implementations of ECC and applications for encryption and digital signatures.
Neo4j MeetUp - Graph Exploration with MetaExpAdrian Ziegler
This document discusses graph exploration using Neo4j and describes:
1. Computing meta-paths from graph schemas to efficiently represent knowledge in graphs.
2. Embedding meta-paths to learn vector representations for active learning and preference prediction.
3. An active learning strategy to label informative meta-paths and explore the space of all meta-paths.
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris
This document proposes methods to incorporate higher-order graph properties into graph neural networks (GNNs). It shows that GNNs are as powerful as the 1-dimensional Weisfeiler-Lehman graph isomorphism test for distinguishing graphs, but cannot capture higher-order properties like triangle counts. The document introduces k-dimensional GNNs and hierarchical k-GNNs to learn representations of subgraphs. Experimental results show these methods improve over 1-GNN baselines on graph classification and regression tasks.
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs Christopher Morris
This document proposes a new graph kernel called the glocalized Weisfeiler-Lehman graph kernel. It extends the classic Weisfeiler-Lehman graph kernel to consider both local and global graph properties. The kernel maps graphs to feature vectors based on the k-dimensional Weisfeiler-Lehman algorithm. Approximation algorithms using adaptive sampling are introduced to make the kernel scalable to large graphs. Experimental results on graph classification benchmarks demonstrate the kernel achieves high accuracy while having fast running times.
We review our recent progress in the development of graph kernels. We discuss the hash graph kernel framework, which makes the computation of kernels for graphs with vertices and edges annotated with real-valued information feasible for large data sets. Moreover, we summarize our general investigation of the benefits of explicit graph feature maps in comparison to using the kernel trick. Our experimental studies on real-world data sets suggest that explicit feature maps often provide sufficient classification accuracy while being computed more efficiently. Finally, we describe how to construct valid kernels from optimal assignments to obtain new expressive graph kernels. These make use of the kernel trick to establish one-to-one correspondences. We conclude by a discussion of our results and their implication for the future development of graph kernels.
This document summarizes and compares two popular Python libraries for graph neural networks - Spektral and PyTorch Geometric. It begins by providing an overview of the basic functionality and architecture of each library. It then discusses how each library handles data loading and mini-batching of graph data. The document reviews several common message passing layer types implemented in both libraries. It provides an example comparison of using each library for a node classification task on the Cora dataset. Finally, it discusses a graph classification comparison in PyTorch Geometric using different message passing and pooling layers on the IMDB-binary dataset.
(DL輪読)Matching Networks for One Shot LearningMasahiro Suzuki
1. Matching Networks is a neural network architecture proposed by DeepMind for one-shot learning.
2. The network learns to classify novel examples by comparing them to a small support set of examples, using an attention mechanism to focus on the most relevant support examples.
3. The network is trained using a meta-learning approach, where it learns to learn from small support sets to classify novel examples from classes not seen during training.
Joint Word and Entity Embeddings for Entity Retrieval from Knowledge GraphFedorNikolaev
The document proposes a method called KEWER that learns distributed representations of words, entities, and categories from a knowledge graph in the same embedding space. KEWER first generates random walks from entities, replaces some elements with surface forms, and then learns embeddings by maximizing the likelihood of contexts. These embeddings improve entity retrieval over term-based and existing joint embedding models, especially when combined with entity linking.
Lec-07: Feature Aggregation and Image Retrieval System [notes]
Image retrieval system performance metrics, precision, recall, true positive rate, false positive rate; Bag of Words (BoW) and VLAD aggregation.
Lec-16: Subspace/Transform Optimization
Address the non-linearity issues in appearance manifolds by having a piece-wise linear solution. Query driven local model learning, subspace indexing on Grassmann manifold, direct Newtonian method of subspace optimization on Grassmann manifold.
Convolutional networks and graph networks through kernelstuxette
This presentation discusses how convolutional kernel networks (CKNs) can be used to model sequential and graph-structured data through kernels defined over sequences and graphs. CKNs define feature maps from substructures like n-mers in sequences and paths in graphs into high-dimensional spaces, which are then approximated to obtain low-dimensional representations that can be used for prediction tasks like classification. This approach is analogous to convolutional neural networks and can be extended to multiple layers. The presentation provides examples showing CKNs achieve good performance on problems involving protein sequences and social networks.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Lec-17: Sparse Signal Processing & Applications [notes]
Sparse signal processing, recovery of sparse signal via L1 minimization. Applications including face recognition, coupled dictionary learning for image super-resolution.
Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian theory and methodology in machine learning. They have achieved remarkable success in computation, and enjoy strong theoretical support. Much of the existing literature has focused on the linear Gaussian case. The purpose of the current talk is to demonstrate that the horseshoe priors are useful more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularization in nonlinear and non-Gaussian models; multivariate models; and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
This document introduces graph neural networks and discusses a claim that they are essentially low-pass filters. It provides an overview of graph neural network operations, including combining node features, aggregating information from neighbors, and updating node representations over multiple layers. The document notes that while graph neural networks may be less powerful than other deep learning methods, they are interesting for problems involving graphs, such as drug discovery and web analytics. It questions how graph neural network classifications operate and whether the low-pass filter behavior is caused by the graph Laplacian matrix.
A short presentation on the emerging research on normalizing flows. The presentations follows two recent survey papers on the topic:
[1] Kobyzev, Ivan, Simon Prince, and Marcus Brubaker. Normalizing flows: An introduction and review of current methods, T-PAMI 2020.
[2] Papamakarios, George, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference, arXiv preprint arXiv:1912.02762 (2019).
AILABS Lecture Series - Is AI The New Electricity. Topic - Deep Learning - Ev...AILABS Academy
Artificial Intelligence is transforming the world. Deep Learning, an integral part of this new Artificial Intelligence paradigm, is becoming one of the most sought after skills. Learn more about Deep Learning and its Evolution.
Multimodal Residual Networks for Visual QAJin-Hwa Kim
Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.
This document provides an overview of elliptic curve cryptography (ECC). It discusses how ECC provides stronger security than RSA with smaller key sizes. The document describes the mathematical foundations of elliptic curves over finite fields. It explains scalar multiplication, which involves adding a point on the elliptic curve to itself multiple times, as the core operation in ECC. Finally, it discusses implementations of ECC and applications for encryption and digital signatures.
Neo4j MeetUp - Graph Exploration with MetaExpAdrian Ziegler
This document discusses graph exploration using Neo4j and describes:
1. Computing meta-paths from graph schemas to efficiently represent knowledge in graphs.
2. Embedding meta-paths to learn vector representations for active learning and preference prediction.
3. An active learning strategy to label informative meta-paths and explore the space of all meta-paths.
This document outlines a tutorial on visual search and understanding. It discusses various techniques for visual representations and indexing, including recent neural network architectures that aim to reduce parameters, memory usage, and spatial redundancy. Specific techniques covered include Multi-Fiber Networks, Double Attention Networks, and Global Reasoning Networks. Global Reasoning Networks are discussed in detail, including how they project features from coordinate space to interaction space, reason over feature interactions using graph convolutions, and project back to coordinate space.
This document discusses the combination of graph neural networks (GNNs) and reinforcement learning (RL). It provides background on GNNs, including how they can handle non-Euclidean data with graph structures. It also describes common GNN models like graph convolutional networks (GCN) and GraphSAGE. The document then reviews previous works that combine GNNs and RL for multi-agent systems and autonomous driving. It presents the graph convolutional reinforcement learning (DGN) framework that uses self-attention and relation kernels to model agent interactions.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Software tookits for machine learning and graphical modelsbutest
This document summarizes machine learning software for graphical models. It discusses discriminative models for independent data, conditional random fields for dependent data, generative models for unsupervised learning, and Bayesian models. It provides an overview of software for inference, learning, and Bayesian inference in graphical models.
This document provides an overview of a tutorial on graph representation learning for recommender systems. The tutorial covers embedding nodes in homogeneous graphs using random walk-based approaches like DeepWalk and node2vec. It also discusses higher-order embedding methods like LINE, which directly model graph properties, and GraRep, which represents the probability of k-step random walks. The graph embeddings can be used for tasks like entity retrieval and classification and as inputs to recommender system models.
This document provides an overview of a tutorial on graph representation learning for recommender systems. The tutorial covers embedding nodes in homogeneous graphs using random walk-based approaches like DeepWalk and node2vec. It also discusses higher-order embedding methods like LINE, which directly model graph properties, and GraRep, which represents the probability of k-step walks between nodes. The graph embeddings can then be used for tasks like recommendation candidate retrieval and as inputs to ranking models.
A simple framework for contrastive learning of visual representationsDevansh16
Link: https://machine-learning-made-simple.medium.com/learnings-from-simclr-a-framework-contrastive-learning-for-visual-representations-6c145a5d8e99
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This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We simplify recently proposed contrastive self-supervised learning algorithms without requiring specialized architectures or a memory bank. In order to understand what enables the contrastive prediction tasks to learn useful representations, we systematically study the major components of our framework. We show that (1) composition of data augmentations plays a critical role in defining effective predictive tasks, (2) introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and (3) contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning. By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet. A linear classifier trained on self-supervised representations learned by SimCLR achieves 76.5% top-1 accuracy, which is a 7% relative improvement over previous state-of-the-art, matching the performance of a supervised ResNet-50. When fine-tuned on only 1% of the labels, we achieve 85.8% top-5 accuracy, outperforming AlexNet with 100X fewer labels.
Comments: ICML'2020. Code and pretrained models at this https URL
Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
Cite as: arXiv:2002.05709 [cs.LG]
(or arXiv:2002.05709v3 [cs.LG] for this version)
Submission history
From: Ting Chen [view email]
[v1] Thu, 13 Feb 2020 18:50:45 UTC (5,093 KB)
[v2] Mon, 30 Mar 2020 15:32:51 UTC (5,047 KB)
[v3] Wed, 1 Jul 2020 00:09:08 UTC (5,829 KB)
1) The document discusses using data in deep learning models, including understanding the limitations of data and how it is acquired.
2) It describes techniques for image matching using multi-view geometry, including finding corresponding points across images and triangulating them to determine camera pose.
3) Recent works aim to improve localization of objects in images using multiple instance learning approaches that can learn without full supervision or through more stable optimization methods like linearizing sampling operations.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
This document discusses knowledge discovery and machine learning on graph data. It makes three main observations:
1) Graphs are typically constructed from input data rather than given directly, as relationships must be inferred.
2) Graph data management is challenging due to issues like large size, dynamic nature, heterogeneity and attribution.
3) Useful insights and accurate modeling depend on the representation of the data as a graph, such as through decomposition, feature learning or other techniques.
The document discusses various methods for 3D object modeling and representation, including:
- Polygonal meshes which approximate surfaces and solids using polygons and can represent a broad class of objects.
- Solid modeling using polygonal meshes where directional information is added to faces using normal vectors.
- Sweep representations that form shapes by extruding or sweeping 2D profiles through space.
- Surface modeling using explicit functions of two variables or surfaces of revolution obtained by rotating curves around axes.
Tutorial on Theory and Application of Generative Adversarial NetworksMLReview
Description
Generative adversarial network (GAN) has recently emerged as a promising generative modeling approach. It consists of a generative network and a discriminative network. Through the competition between the two networks, it learns to model the data distribution. In addition to modeling the image/video distribution in computer vision problems, the framework finds use in defining visual concept using examples. To a large extent, it eliminates the need of hand-crafting objective functions for various computer vision problems. In this tutorial, we will present an overview of generative adversarial network research. We will cover several recent theoretical studies as well as training techniques and will also cover several vision applications of generative adversarial networks.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Deep learning for molecules, introduction to chainer chemistryKenta Oono
1) The document introduces machine learning and deep learning techniques for predicting chemical properties, including rule-based approaches versus learning-based approaches using neural message passing algorithms.
2) It discusses several graph neural network models like NFP, GGNN, WeaveNet and SchNet that can be applied to molecular graphs to predict characteristics. These models update atom representations through message passing and graph convolution operations.
3) Chainer Chemistry is introduced as a deep learning framework that can be used with these graph neural network models for chemical property prediction tasks. Examples of tasks include drug discovery and molecular generation.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
The document discusses machine learning techniques for graphs and graph-parallel computing. It describes how graphs can model real-world data with entities as vertices and relationships as edges. Common machine learning tasks on graphs include identifying influential entities, finding communities, modeling dependencies, and predicting user behavior. The document introduces the concept of graph-parallel programming models that allow algorithms to be expressed by having each vertex perform computations based on its local neighborhood. It presents examples of graph algorithms like PageRank, product recommendations, and identifying leaders that can be implemented in a graph-parallel manner. Finally, it discusses challenges of analyzing large real-world graphs and how systems like GraphLab address these challenges through techniques like vertex-cuts and asynchronous execution.
Similar to JOSA TechTalks - Machine Learning on Graph-Structured Data (20)
Much of the software world has moved to microservices and a Service-Oriented Architecture (SOA) when designing large-scale systems. An alternate, less-known philosophy of building large systems is called Data-Oriented Architecture (DOA), which trades off the scale and maintainability challenges of SOA with a rigorous focus on schema representation in a monolithic data access layer. For microservices-based systems where scale across verticals is a challenge, DOA might be an appropriate alternative.
Speaker: Eyas Sharaiha - Senior Software Engineer at Google
Open source is an important part of the engineering infrastructure at OpenSooq, and mobile is no exception. OpenSooq team presents their development toolbox, modules they use, and how OpenSooq tests its apps at scale and speed.
Presentation about digital transformation driven by big data, and how to navigate from data to insight to action. Presentation given by Hamzah Amin, a Senior Data Scientist & Analytics Consultant at Jordan Business Systems. He is also a Master's student in Data Science at Princess Sumaya University for Technology.
By Hamzah Amin at the JOSA Data Science Meetup on 14/9/2019.
Applications of Data Science in Drug Discovery, Financial Services, Project Management, Human Resources and Marketing.
By Dr. Laila Alabidi at the JOSA Data Science Meetup on 17/8/2019.
Slides for 'JOSA TechTalks: Arabic NLP in Practice' with an introduction to Natural Language Understanding (NLU) with focus on Arabic, covers main issues in (Arabic) NLU and used tools and resources.
By Dr. Samir Tartir - Senior Research Scientist of AI at Mawdoo3
Docker is an open platform for developers and system administrators to build, ship and run distributed applications. Using Docker, companies in Jordan have been able to build powerful system architectures that allow speeding up delivery, easing deployment processes and at the same time cutting major hosting costs.
Osama Jaber shares his experience at ArabiaWeather in how they moved away from AWS to a highly-redundant, high-performance and low-cost solution using docker and other open-source technologies.
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In this session, Sinan introduces Supervised Learning, help you build an intuition about it, and walk you through an example with Python using scikit-learn. You'll see it is pretty straightforward, and you might find room to apply Supervised Learning in your current or next project!
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2. Agenda
● Background & Motivation
● [Breadth] ML Models on Graphs
● [Depth] Recent ML Models on Graphs
○ MixHop (ICML’19)
○ Watch Your Step (NeurIPS’18)
● Fast Training
○ GTTF (ICLR’21)
○ Fast GRL with unique optimal solutions (ICLR’21 Workshop GTRL)
2
5. What is a graph?
5
Nodes: entities
Edges: relationships between entities
6. What is a graph?
6
Nodes: entities
Edges: relationships between entities
x
x x
x x x
x
x
x
x
y
y: labels
y
y
x
x
x: features
7. What is a graph?
7
nodes = people
edges = friendship
Social Network
y= engaging ads
x=[age, gender, .]
8. What is a graph?
8
News Articles
nodes = articles
edges = citations
y=article type
x=article text
9. What is a graph?
Chemical compounds can be viewed as a graph:
9
y=molecule properties
(per graph)
x=[H, F, C, O,
N,.]
10. Why ML on Graphs?
Motivation
10
Across domains, practitioners benefit from predictions on graphs.
Some Popular Tasks:
● Predict node labels (node classification)
○ E.g., predict users engagement to ads (in a social network).
● Predict missing edges (link prediction / edge classification)
○ E.g., predict which proteins interact with each other.
● Classify an entire graph
○ E.g., predict physical properties of a chemical molecule represented as a graph
● Generate Graphs [e.g. with certain properties]
○ E.g., can answer “Give me a chemical molecule with the following properties”
11. High Level of Various Graph Algorithms
Fine! You have a graph.
You want to predict information on the graph. How to proceed?
Next: Identify the modeling technique!
● Option (Graph Embeddings): Place nodes onto an embedding space → throw the
graph away but keep embeddings.
● Option (Graph Regularization): Use graph as a regularizer. No graph is needed
after model training.
● Option (Graph Convolution ⊂ Message Passing). Representation of a node is a
function of its neighbors. Graph is needed for training and inference.
13. (undirected) Graph
Adjacency Matrix
Degree Matrix
Feature Matrix
Transition Matrix
Laplacian Matrix
Quiz: What does TX encode?
L gives relaxed estimates to NP-Hard Problems e.g. Graph Partitioning.
Its eigenbasis provide an a continuous axes on which nodes live
15. High Level of Various Graph Algorithms
15
● Option (Graph Embeddings)
● Option (Graph Regularization)
● Option (Graph Convolution ⊂ Message Passing)
16. Overview: Graph Embedding
v1 v2
v3 v5
v4
v6
v11
v9
.v1
.v11 .v6
.v2
.v3
.v4
.v5
.v9
Embed in Rd
Factorize A or L [1]
Auto Encode A [2]
Skipgram on E[walk] [3, B, D]
[1] Belkin & Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation 2013
[2] Wang et al, Structural Deep Network Embedding, KDD’2016
[3] Perozzi et al, DeepWalk, KDD’2014
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
[D] Abu-El-Haija et al, Learning Edge Representations via Low-Rank Asymmetric Projections, CIKM’2017
[E] Lee, Abu-El-Haija, Varadarajan, Natsev, Collaborative Deep Metric Learning for Video Understanding, KDD’2018
16
17. Overview: Graph Embedding
v1 v2
v3 v5
v4
v6
v11
v9
.v1
.v11 .v6
.v2
.v3
.v4
.v5
.v9
Embed in Rd
Factorize A or L [1]
Auto Encode A [2]
Skipgram on E[walk] [3, B, D]
[1] Belkin & Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation 2013
[2] Wang et al, Structural Deep Network Embedding, KDD’2016
[3] Perozzi et al, DeepWalk, KDD’2014
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
[D] Abu-El-Haija et al, Learning Edge Representations via Low-Rank Asymmetric Projections, CIKM’2017
[E] Lee, Abu-El-Haija, Varadarajan, Natsev, Collaborative Deep Metric Learning for Video Understanding, KDD’2018
17
Random Walk
v3 → v5 → v9 → v11 → v5 →
...
…
...
Random Walk Sequences
word2vec algorithm
18. Review: Embedding via Random Walks
● Word2vec learns word embeddings by stochastically moving
embedding of an anchor node closer to a neighboring context
node.
v3 → v5 → v9 → v11 → v5 → ...
Random Walk Sequences Embeddings Y
18
.v1
.v11 .v6
.v2
.v3
.v4
.v5
x
y
.v9
19. Review: Embedding via Random Walks
● Word2vec learns word embeddings by stochastically moving
embedding of an anchor node closer to a neighboring context
node.
Mikolov et al. Distributed Representations of Words and Phrases and their Compositionality. NIPS 2013
v3 → v5 → v9 → v11 → v5 → ...
Random Walk Sequences Embeddings Y
19
anchor
node
.v1
.v11 .v6
.v2
.v3
.v4
.v5
.v9
x
y
20. Review: Embedding via Random Walks
● Word2vec learns word embeddings by stochastically moving
embedding of an anchor node closer to a neighboring context
node.
v3 → v5 → v9 → v11 → v5 → ...
Random Walk Sequences Embeddings Y
20
anchor
node
context
node
.v1
.v11 .v6
.v2
.v3
.v4
.v5
.v9
x
y
Mikolov et al. Distributed Representations of Words and Phrases and their Compositionality. NIPS 2013
21. Review: Embedding via Random Walks
● Word2vec learns word embeddings by stochastically moving
embedding of an anchor node closer to a neighboring context
node.
Mikolov et al. Distributed Representations of Words and Phrases and their Compositionality. NIPS 2013
v3 → v5 → v9 → v11 → v5 → ...
Random Walk Sequences Embeddings Y
21
anchor
node
context
node
.v1
.v11 .v6
.v2
.v3
.v4
.v5
.v9
x
y
Stochastic
Update
22. High Level of Various Graph Algorithms
● Option (Graph Embeddings)
● Option (Graph Regularization)
● Option (Graph Convolution ⊂ Message Passing)
22
23. Overview: Graph Regularization
v1 v2
v3 v5
v4
v6
v11
v9
x11
x6
fΘ : X → Y
h11
h6
2
l2
minΘ λ - y6 log h6 - y11 log h11
23
[4] Belkin et al, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR’2006.
[5] Bui et al, Neural Graph Machines, Arxiv’17
24. Overview: Graph Regularization
v1 v2
v3 v5
v4
v6
v11
v9
x11
x6
fΘ : X → Y
fΘ(x6)
2
l2
minΘ - y6 log fΘ(x6) - y11 log fΘ(x11)
fΘ(x11
)
λ
[4] Belkin et al, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR’2006.
[5] Bui et al, Neural Graph Machines, Arxiv’17
24
25. Overview: Graph Regularization
v1 v2
v3 v5
v4
v6
v11
v9
x11
x6
fΘ : X → Y
fΘ(x6)
2
l2
minΘ - y6 log fΘ(x6) - y11 log fΘ(x11)
fΘ(x11
)
λ
f(xi)
2
l2
minΘ Ai, j - yi log f(xi) - yj log f(xj)
f(xj)
Σi, j λ
Overall Objective:
[4] Belkin et al, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR’2006.
[5] Bui et al, Neural Graph Machines, Arxiv’17
25
26. High Level of Various Graph Algorithms
26
● Option (Graph Embeddings)
● Option (Graph Regularization)
● Option (Graph Convolution ⊂ Message Passing)
27. Overview: Message Passing
The first neural network on graph data (I am aware of)
[6] Scarselli et al. The graph neural network model. IEEE Transactions on Neural Networks’2009
30. Watch Your Step (Node Embedding Method)
Watch Your Step learns the context distribution (while
learning the embeddings):
Shortcoming of DeepWalk (/ node2vec): they have a fixed Context Distribution
controlled by hyperparameter (C) context window size. Graphs prefer different C:
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
31. WatchYourStep (WYS): Derivation
Rather than factorizing:
31
or:
Into low-rank L x RT, with objective:
WYS Factorizes:
Additionally training Q “the context distribution”
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
32. WYS Results: Link Prediction
32
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
33. WYS Results: Node Classification & T-SNE plot
33
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
34. WYS Experiments: What does Q learn?
Different distribution for different graph!
34
Correspond to manual sweeping of node2vec:
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
37. Recall: Image Convolution
● State-of-the-art on image / video / speech.
○ (segmentation, detection, classification, etc).
input
2D (Spatial) Convolutional Layer: Representing image as a regular grid
4D trainable filter
output
vectors
Message Passing
*
38. [H] Chami, Abu-El-Haija, Perozzi, Re, Murphy, Machine Learning on Graphs: A Model and Comprehensive Taxonomy, arxiv’2020
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
● There are multiple definitions we survey in [H]
● For now, we stick to the most popular [7] (=[61] above)
What is Graph Convolutions
39. GCN [7] for semi-supervised node classification
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
41. GCN [7] for semi-supervised node classification
x1
x3
x5
x6
Input Features
x4
x2
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
42. GCN [7] for semi-supervised node classification
x1
x3
x5
x6
Input Features
x4
x2
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
y2
y4
Some nodes are labeled
Task: Can we guess label of
unlabeled nodes?
43. GCN [7] for semi-supervised node classification
GC Layer 1
x1
x3
x5
x6
Input Features
x4
x2
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
59. MixHop
MixHop GC Layer
😀 Can incorporate distant nodes
61
[C] Abu-El-Haija et al, MixHop, ICML 2019
60. MixHop
MixHop GC Layer
😀 Can incorporate distant nodes
😀 Can mix neighbors across distances
i.e. can learn Gabor Filters!
62
[C] Abu-El-Haija et al, MixHop, ICML 2019
62. [G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
63. Goal of GTTF
● Take any Graph Learning Algorithm.
● Re-write it using “GTTF” functions (AccumulateFn and BiasFn)
● This makes the algorithm scalable to arbitrarily large graphs!
64. GTTF
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
65. [G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
66. Graph Convolution on top of GTTF
Define the GTTF functions:
Run model on sampled (rooted) Adjacency:
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
67. Node Embeddings on top of GTTF
Define the accumulation function (No Bias Fn)
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
69. Algorithms on top of GTTF are scalable
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
70. GTTF: Scale Performance Experiments [G]
76
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
71. GTTF: Test Metrics Experiments
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
72. [J] Abu-El-Haija et al, Fast Graph Learning with Unique Optimal Solutions, ICLR’21 GTRL
73. What is SVD?
[J] Abu-El-Haija et al, Fast Graph Learning with Unique Optimal
Solutions, arxiv 2021
74. We open-source a Functional SVD for TensorFlow
https://github.com/samihaija/tf-fsvd. Useful if:
● You want to run SVD on a sparse matrix in TensorFlow (our code, out of the
box, provides a specialization of tf.linalg.svd onto sparse matrices)
● You want to run SVD on a dense matrix M (that is expensive to compute).
However, your matrix M is structured (e.g. geometric sum of sparse matrices),
such that, multiplying M by vectors is much cheaper than explicitly
constructing M.
75. SVD for Graph Learning
● SVD can be used as ML technique for graphs
○ Steps:
■ Linearize models.
■ Make objective function convex.
● We show this next, for two popular techniques
81. [A] Abu-El-Haija et al, YouTube-8M: A Large-Scale Video Classification Benchmark, Arxiv’2016
[B] Abu-El-Haija et al, Watch Your Step: Learning Node Embeddings via Graph Attention, NeurIPS’2018
[C] Abu-El-Haija,…, Ver Steeg, Aram Galstyan, MixHop: Higher-Order Graph Convolution, ICML’2019.
[D] Abu-El-Haija et al, Learning Edge Representations via Low-Rank Asymmetric Projections, CIKM’2017
[E] Lee, Abu-El-Haija, Varadarajan, Natsev, Collaborative Deep Metric Learning for Video Understanding, KDD’2018
[F] Ge, Abu-El-Haija, Xin, Itti, Zero-shot Synthesis with Group-Supervised Learning, ICLR’2021
[G] Markowitz* et al, Graph traversal with tensor functionals: a meta-algorithm for scalable learning, ICLR’2021
[H] Chami, Abu-El-Haija, Perozzi, Re, Murphy, Machine Learning on Graphs: A Model and Comprehensive Taxonomy, arxiv’2020
[I] Abu-El-Haija et al, N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification, UAI’2019
[J] Abu-El-Haija et al, Fast Graph Learning with Unique Optimal Solutions, arxiv 2021
[1] Belkin & Niyogi, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Neural Computation’2013
[2] Wang et al, Structural Deep Network Embedding, KDD’2016
[3] Perozzi et al, DeepWalk, KDD’2014
[4] Belkin et al, Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR’2006.
[5] Bui et al, Neural Graph Machines, Arxiv’17
[6] Scarselli et al, The graph neural network model, IEEE Transactions on Neural Networks’2009
[7] Kipf & Welling, Semi-supervised classification with graph convolutional networks, ICLR’2017
[8] Daugman, Two-dimensional spectral analysis of cortical receptive field profiles, Vision Research’1980
[9] Daugman, Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by ..., JOSA’1985
[10] Honglak Lee et al, ICML’2009
[11] Alex Krizhevsky et al, NeurIPS’2012
[12] Gordon et al, MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks, CVPR’2018
References
83. We add group L2-Lasso
Regularization to drop-out columns
feature matrices, similar to [12]
[12] Gordon et al, MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks, CVPR’2018
[images are rotated for space]
89
MixHop Sparsification
84. MixHop Sparsification
We add group L2-Lasso
Regularization to drop-out columns
feature matrices, similar to [12]
2nd layer of Cora drops-out zeroth-
power completely.
[images are rotated for space]
[12] Gordon et al, MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks, CVPR’2018
90
87. MixHop Results on (Synthetic) Homophily Datasets
With less homophily, our
performance gap increases
88. MixHop Results on (Synthetic) Homophily Datasets
With less homophily, our
performance gap increases
With less homophily, our method
learns more feature differences
(i.e. Gabor-like Filters)
90. [F] Ge, Abu-El-Haija, Xin, Itti, Zero-shot Synthesis with Group-Supervised Learning, ICLR’2021
Ad: Message Passing for Zero-Shot Synthesis
Graph of semantic similarity
between training samples
We can develop an auto-enocder with a
disentangled feature space.
If two samples share one attribute value (per
graph edge), they need to prove it:
96
Editor's Notes
Data structure that can represent entities and their relationships.
Data structure that can represent entities and their relationships.
Many random walks == Many (long) Sequences
Current embedding
Sample context node, within distance from anchor node.
Sample context node, within distance from anchor node.