This document introduces active learning, which is a machine learning method that can provide substantial improvements in classification accuracy with fewer labeled examples. It describes how active learning works by allowing the learning algorithm to interactively query an oracle to label unlabeled examples that it believes will be most informative to learn the target function. Several strategies for querying examples are discussed, including uncertainty sampling, query-by-committee, and density-weighted methods. Real-world applications of active learning to tasks like sentence boundary detection, document ranking, spam filtering, and pornography detection are also briefly outlined.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Koss 6 a17_deepmachinelearning_mariocho_r10Mario Cho
This document provides an overview of deep machine learning and its applications. It discusses neural networks, deep learning techniques, and Mario Cho's experience in areas like image recognition, medical imaging, and open source software development. Deep learning can be used for tasks like image recognition, language translation, game playing, and more. Neural networks can learn from large amounts of data to perform complex human-like tasks without being explicitly programmed.
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
This chapter introduces the theoretical foundations of knowledge mining and intelligent agents. It discusses key concepts like knowledge, intelligent agents, and the fundamental tasks of knowledge discovery in databases. The chapter also provides an overview of several well-developed intelligent agent methodologies, including ant colony optimization, particle swarm optimization, and evolutionary algorithms that can be used for knowledge mining.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
DataEngConf: Feature Extraction: Modern Questions and Challenges at GoogleHakka Labs
By Dmitry Storcheus (Engineer, Google Research)
Feature extraction, as usually understood, seeks an optimal transformation from raw data into features that can be used as an input for a learning algorithm. In recent times this problem has been attacked using a growing number of diverse techniques that originated in separate research communities: from PCA and LDA to manifold and metric learning. The goal of this talk is to contrast and compare feature extraction techniques coming from different machine learning areas as well as discuss the modern challenges and open problems in feature extraction. Moreover, this talk will suggest novel solutions to some of the challenges discussed, particularly to coupled feature extraction.
This document summarizes a research paper on DeepFix, a fully convolutional neural network for predicting human eye fixations. DeepFix uses a very deep network with 20 layers and small kernel sizes, inspired by VGG nets. It is a fully convolutional network with convolutional layers replacing fully connected layers to capture global context. The network includes inception layers with parallel kernels of different sizes, and location biased convolutional layers to introduce a center bias. The network is trained end-to-end on datasets of human eye fixations to predict heatmaps of fixation locations. It achieves state-of-the-art results, training in one day on a K40 GPU.
Slides by Amaia Salvador at the UPC Computer Vision Reading Group.
Source document on GDocs with clickable links:
https://docs.google.com/presentation/d/1jDTyKTNfZBfMl8OHANZJaYxsXTqGCHMVeMeBe5o1EL0/edit?usp=sharing
Based on the original work:
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks." In Advances in Neural Information Processing Systems, pp. 91-99. 2015.
Koss 6 a17_deepmachinelearning_mariocho_r10Mario Cho
This document provides an overview of deep machine learning and its applications. It discusses neural networks, deep learning techniques, and Mario Cho's experience in areas like image recognition, medical imaging, and open source software development. Deep learning can be used for tasks like image recognition, language translation, game playing, and more. Neural networks can learn from large amounts of data to perform complex human-like tasks without being explicitly programmed.
Slides by Miriam Bellver from the Computer Vision Reading Group at the Universitat Politecnica de Catalunya about the paper:
Lu, Yongxi, Tara Javidi, and Svetlana Lazebnik. "Adaptive Object Detection Using Adjacency and Zoom Prediction." CVPR 2016
Abstract:
State-of-the-art object detection systems rely on an accurate set of region proposals. Several recent methods use a neural network architecture to hypothesize promising object locations. While these approaches are computationally efficient, they rely on fixed image regions as anchors for predictions. In this paper we propose to use a search strategy that adaptively directs computational resources to sub-regions likely to contain objects. Compared to methods based on fixed anchor locations, our approach naturally adapts to cases where object instances are sparse and small. Our approach is comparable in terms of accuracy to the state-of-the-art Faster R-CNN approach while using two orders of magnitude fewer anchors on average. Code is publicly available.
This chapter introduces the theoretical foundations of knowledge mining and intelligent agents. It discusses key concepts like knowledge, intelligent agents, and the fundamental tasks of knowledge discovery in databases. The chapter also provides an overview of several well-developed intelligent agent methodologies, including ant colony optimization, particle swarm optimization, and evolutionary algorithms that can be used for knowledge mining.
An Introduction to Supervised Machine Learning and Pattern Classification: Th...Sebastian Raschka
The document provides an introduction to supervised machine learning and pattern classification. It begins with an overview of the speaker's background and research interests. Key concepts covered include definitions of machine learning, examples of machine learning applications, and the differences between supervised, unsupervised, and reinforcement learning. The rest of the document outlines the typical workflow for a supervised learning problem, including data collection and preprocessing, model training and evaluation, and model selection. Common classification algorithms like decision trees, naive Bayes, and support vector machines are briefly explained. The presentation concludes with discussions around choosing the right algorithm and avoiding overfitting.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
DataEngConf: Feature Extraction: Modern Questions and Challenges at GoogleHakka Labs
By Dmitry Storcheus (Engineer, Google Research)
Feature extraction, as usually understood, seeks an optimal transformation from raw data into features that can be used as an input for a learning algorithm. In recent times this problem has been attacked using a growing number of diverse techniques that originated in separate research communities: from PCA and LDA to manifold and metric learning. The goal of this talk is to contrast and compare feature extraction techniques coming from different machine learning areas as well as discuss the modern challenges and open problems in feature extraction. Moreover, this talk will suggest novel solutions to some of the challenges discussed, particularly to coupled feature extraction.
This document summarizes a research paper on DeepFix, a fully convolutional neural network for predicting human eye fixations. DeepFix uses a very deep network with 20 layers and small kernel sizes, inspired by VGG nets. It is a fully convolutional network with convolutional layers replacing fully connected layers to capture global context. The network includes inception layers with parallel kernels of different sizes, and location biased convolutional layers to introduce a center bias. The network is trained end-to-end on datasets of human eye fixations to predict heatmaps of fixation locations. It achieves state-of-the-art results, training in one day on a K40 GPU.
The document provides an overview of deep learning including:
- Its roots in neural networks and how it creates many layers of neurons to learn structured representations of big data
- Key deep learning algorithms like convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
- Applications that have been advanced by deep learning, including computer vision, speech recognition, natural language processing and control
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
This document summarizes image segmentation techniques using deep learning. It begins with an overview of semantic segmentation and instance segmentation. It then discusses several techniques for semantic segmentation, including deconvolution/transposed convolution for learnable upsampling, skip connections to combine predictions from different CNN depths, and dilated convolutions to increase the receptive field without losing resolution. For instance segmentation, it covers proposal-based methods like Mask R-CNN, and single-shot and recurrent approaches as alternatives to proposal-based models.
Deep Learning Cases: Text and Image ProcessingGrigory Sapunov
Deep learning has achieved superhuman performance on tasks like image classification, object detection, and traffic sign recognition. Several examples are provided, including algorithms that outperform humans on German traffic sign recognition by 2-6 times. Deep learning has also been applied to tasks involving text, video, speech recognition and generation, question answering, and reinforcement learning. Libraries and frameworks like TensorFlow and Caffe have helped spread deep learning techniques.
The document discusses analyzing sentiment and classification using neural network approaches. It begins by introducing the concepts of machine learning models, training and evaluation data, and model training and evaluation. It then discusses applications of sentiment analysis and classification to movie reviews, including describing commonly used datasets and evaluation metrics. Finally, it outlines different neural network architectures that can be used for sentiment analysis and classification tasks, including convolutional and recurrent neural networks.
Numenta engineer Yuwei Cui walks through how the HTM Spatial Pooler works, explaining why desired properties exist and how they work. Includes lots of graphs of SP online learning performance, discussion of topology and boosting.
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.
The document discusses deep learning and learning hierarchical representations. It makes three key points:
1. Deep learning involves learning multiple levels of representations or features from raw input in a hierarchical manner, unlike traditional machine learning which uses engineered features.
2. Learning hierarchical representations is important because natural data lies on low-dimensional manifolds and disentangling the factors of variation can lead to more robust features.
3. Architectures for deep learning involve multiple levels of non-linear feature transformations followed by pooling to build increasingly abstract representations at each level. This allows the representations to become more invariant and disentangled.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
The document discusses recommender systems and summarizes:
1) It introduces recommender systems and the different types including knowledge-based, collaborative filtering, and content-based recommendations.
2) It outlines some of the key resources for recommender systems including datasets, conferences, and articles.
3) It provides a high-level overview of common recommender system approaches like collaborative filtering, content-based analysis, and knowledge-based recommendations.
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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.
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Using Deep Learning to do Real-Time Scoring in Practical Applications - 2015-...Greg Makowski
This talk covers 4 configurations of deep learning to solve different types of application needs. Also, strategies for speed up and real-time scoring are discussed.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
This document summarizes Kevin McGuinness' presentation on deep learning for computer vision. It discusses visual attention models and their ability to predict eye gaze, applications in image cropping, retrieval and classification. It also covers medical image analysis using deep learning for knee osteoarthritis grading and neonatal brain segmentation. Deep crowd analysis is examined for crowd counting. Finally, interactive deep vision for image segmentation using user interactions is presented.
This document provides an overview of a Machine Learning course, including:
- The course is taught by Max Welling and includes homework, a project, quizzes, and a final exam.
- Topics covered include classification, neural networks, clustering, reinforcement learning, Bayesian methods, and more.
- Machine learning involves computers learning from data to improve performance and make predictions. It is a subfield of artificial intelligence.
Presentation on Machine Learning and Data Miningbutest
The document discusses the differences between automatic learning/machine learning and data mining. It provides definitions for supervised vs unsupervised learning, what automated induction is, and the base components of data mining. Additionally, it outlines differences in the scientific approach between automatic learning and data mining, as well as differences from an industry perspective, including common data mining techniques used and tips for successful data mining projects.
The document provides an overview of deep learning including:
- Its roots in neural networks and how it creates many layers of neurons to learn structured representations of big data
- Key deep learning algorithms like convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
- Applications that have been advanced by deep learning, including computer vision, speech recognition, natural language processing and control
Deep Learning And Business Models (VNITC 2015-09-13)Ha Phuong
Deep Learning and Business Models
Tran Quoc Hoan discusses deep learning and its applications, as well as potential business models. Deep learning has led to significant improvements in areas like image and speech recognition compared to traditional machine learning. Some business models highlighted include developing deep learning frameworks, building hardware optimized for deep learning, using deep learning for IoT applications, and providing deep learning APIs and services. Deep learning shows promise across many sectors but also faces challenges in fully realizing its potential.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
This document summarizes image segmentation techniques using deep learning. It begins with an overview of semantic segmentation and instance segmentation. It then discusses several techniques for semantic segmentation, including deconvolution/transposed convolution for learnable upsampling, skip connections to combine predictions from different CNN depths, and dilated convolutions to increase the receptive field without losing resolution. For instance segmentation, it covers proposal-based methods like Mask R-CNN, and single-shot and recurrent approaches as alternatives to proposal-based models.
Deep Learning Cases: Text and Image ProcessingGrigory Sapunov
Deep learning has achieved superhuman performance on tasks like image classification, object detection, and traffic sign recognition. Several examples are provided, including algorithms that outperform humans on German traffic sign recognition by 2-6 times. Deep learning has also been applied to tasks involving text, video, speech recognition and generation, question answering, and reinforcement learning. Libraries and frameworks like TensorFlow and Caffe have helped spread deep learning techniques.
The document discusses analyzing sentiment and classification using neural network approaches. It begins by introducing the concepts of machine learning models, training and evaluation data, and model training and evaluation. It then discusses applications of sentiment analysis and classification to movie reviews, including describing commonly used datasets and evaluation metrics. Finally, it outlines different neural network architectures that can be used for sentiment analysis and classification tasks, including convolutional and recurrent neural networks.
Numenta engineer Yuwei Cui walks through how the HTM Spatial Pooler works, explaining why desired properties exist and how they work. Includes lots of graphs of SP online learning performance, discussion of topology and boosting.
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.
The document discusses deep learning and learning hierarchical representations. It makes three key points:
1. Deep learning involves learning multiple levels of representations or features from raw input in a hierarchical manner, unlike traditional machine learning which uses engineered features.
2. Learning hierarchical representations is important because natural data lies on low-dimensional manifolds and disentangling the factors of variation can lead to more robust features.
3. Architectures for deep learning involve multiple levels of non-linear feature transformations followed by pooling to build increasingly abstract representations at each level. This allows the representations to become more invariant and disentangled.
Deep Learning is the area of machine learning and one of the most talked about trends in business and computer science today.
In this talk, I will give a review of Deep Learning explaining what it is, what kinds of tasks it can do today, and what it probably could do in the future.
The document discusses recommender systems and summarizes:
1) It introduces recommender systems and the different types including knowledge-based, collaborative filtering, and content-based recommendations.
2) It outlines some of the key resources for recommender systems including datasets, conferences, and articles.
3) It provides a high-level overview of common recommender system approaches like collaborative filtering, content-based analysis, and knowledge-based recommendations.
Zaikun Xu from the Università della Svizzera Italiana presented this deck at the 2016 Switzerland HPC Conference.
“In the past decade, deep learning as a life-changing technology, has gained a huge success on various tasks, including image recognition, speech recognition, machine translation, etc. Pio- neered by several research groups, Geoffrey Hinton (U Toronto), Yoshua Benjio (U Montreal), Yann LeCun(NYU), Juergen Schmiduhuber (IDSIA, Switzerland), Deep learning is a renaissance of neural network in the Big data era.
Neural network is a learning algorithm that consists of input layer, hidden layers and output layers, where each circle represents a neural and the each arrow connection associates with a weight. The way neural network learns is based on how different between the output of output layer and the ground truth, following by calculating the gradients of this discrepancy w.r.b to the weights and adjust the weight accordingly. Ideally, it will find weights that maps input X to target y with error as lower as possible.”
Watch the video presentation: http://insidehpc.com/2016/03/deep-learning/
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
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.
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Using Deep Learning to do Real-Time Scoring in Practical Applications - 2015-...Greg Makowski
This talk covers 4 configurations of deep learning to solve different types of application needs. Also, strategies for speed up and real-time scoring are discussed.
“Automatically learning multiple levels of representations of the underlying distribution of the data to be modelled”
Deep learning algorithms have shown superior learning and classification performance.
In areas such as transfer learning, speech and handwritten character recognition, face recognition among others.
(I have referred many articles and experimental results provided by Stanford University)
This document summarizes Kevin McGuinness' presentation on deep learning for computer vision. It discusses visual attention models and their ability to predict eye gaze, applications in image cropping, retrieval and classification. It also covers medical image analysis using deep learning for knee osteoarthritis grading and neonatal brain segmentation. Deep crowd analysis is examined for crowd counting. Finally, interactive deep vision for image segmentation using user interactions is presented.
This document provides an overview of a Machine Learning course, including:
- The course is taught by Max Welling and includes homework, a project, quizzes, and a final exam.
- Topics covered include classification, neural networks, clustering, reinforcement learning, Bayesian methods, and more.
- Machine learning involves computers learning from data to improve performance and make predictions. It is a subfield of artificial intelligence.
Presentation on Machine Learning and Data Miningbutest
The document discusses the differences between automatic learning/machine learning and data mining. It provides definitions for supervised vs unsupervised learning, what automated induction is, and the base components of data mining. Additionally, it outlines differences in the scientific approach between automatic learning and data mining, as well as differences from an industry perspective, including common data mining techniques used and tips for successful data mining projects.
Speaker: Pierre Richemond, Data Science Institute of Imperial College
Title: Cutting edge generative models: Applications and implications
Abstract: This talk will examine recent developments in deep learning content generation at scale. Whether it be images or text, the latest methods have now reached a level of quality making it hard to discriminate between human- and AI-generated content. We will review recent examples of such generative models, and put their significance in a broader context, in light of such powerful tools’ potential for dual use.
Bio: Pierre is currently researching his PhD in deep reinforcement learning at the Data Science Institute of Imperial College. He also teaches Deep Learning at the Graduate School, and helps to run the Deep Learning Network and organises thematic reading groups. His background is in mathematics - he has studied electrical engineering at ENST, probability theory and stochastic processes at Universite Paris VI - Ecole Polytechnique, and business management at HEC.
TMS workshop on machine learning in materials science: Intro to deep learning...BrianDeCost
This presentation is intended as a high-level introduction for to deep learning and its applications in materials science. The intended audience is materials scientists and engineers
Disclaimers: the second half of this presentation is intended as a broad overview of deep learning applications in materials science; due to time limitations it is not intended to be comprehensive. As a review of the field, this necessarily includes work that is not my own. If my own name is not included explicitly in the reference at the bottom of a slide, I was not involved in that work.
Any mention of commercial products in this presentation is for information only; it does not imply recommendation or endorsement by NIST.
This document provides an overview of probabilistic approaches to information retrieval. It discusses why probabilities are useful for IR given the inherent uncertainty. It covers the Probability Ranking Principle, which aims to rank documents by estimated probability of relevance. Other probabilistic techniques discussed include probabilistic indexing, probabilistic inference using logic representations, and using Bayesian networks for IR. The document notes open issues with some of these approaches and concludes by surveying existing survey papers on probabilistic IR.
Deep learning is finding applications in science such as predicting material properties. DLHub is being developed to facilitate sharing of deep learning models, data, and code for science. It will collect, publish, serve, and enable retraining of models on new data. This will help address challenges of applying deep learning to science like accessing relevant resources and integrating models into workflows. The goal is to deliver deep learning capabilities to thousands of scientists through software for managing data, models and workflows.
Xin Yao: "What can evolutionary computation do for you?"ieee_cis_cyprus
Evolutionary computation techniques like genetic programming and evolutionary algorithms can be used for adaptive optimization, data mining, and machine learning. They have been successfully applied to problems like modeling galaxy distributions, material modeling, constraint handling, dynamic optimization, multi-objective optimization, and ensemble learning. While evolutionary computation has had many real-world applications, challenges remain in improving theoretical foundations, scalability to large problems, dealing with dynamic and uncertain environments, and developing the ability to learn from previous optimization experiences.
1. Machine learning involves using algorithms to learn from data without being explicitly programmed. It is an interdisciplinary field that draws from statistics, computer science, and many other areas.
2. There are massive amounts of data being generated every day from sources like Google, Facebook, YouTube, and more. This data provides opportunities for machine learning applications.
3. Machine learning tasks can be supervised, involving labeled example data, or unsupervised, involving unlabeled data. Supervised learning predicts labels for new data based on patterns in labeled training data, while unsupervised learning finds hidden patterns in unlabeled data.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
This document summarizes a study of deep learning models and Bayesian statistics. It discusses the history of artificial intelligence and machine learning before introducing restricted Boltzmann machines, deep belief networks, and Bayesian statistics. It describes experiments applying restricted Boltzmann machines to classify movies and generate images, and using a deep belief network to classify images from multiple datasets with 100% accuracy. The conclusion states that deep learning has advanced artificial intelligence by allowing algorithms to perform multiple tasks and taken us closer to the original goal of general artificial intelligence.
The document discusses using MongoDB as a database for materials discovery projects at Lawrence Berkeley National Lab. It describes how MongoDB is used to [1] store and manage computational calculations across multiple supercomputers, [2] store complex materials data in flexible documents, and [3] enable powerful querying and analysis of the data through logging and machine learning. MongoDB allows for integrated management, analysis and automation of computational materials science projects.
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
This document outlines clustering algorithms for large datasets. It discusses k-means clustering and extensions like k-means++ that improve initialization. It also covers spectral relaxation methods that reformulate k-means as a trace maximization problem to address local minima. Additionally, it proposes landmark-based clustering algorithms for biological sequences that select landmarks in one pass and assign sequences to the nearest landmark using hashing to search for neighbors. The document provides analysis of the time and space complexity of these algorithms as well as assumptions about separability and cluster size.
Artificial Intelligence and its applicationFELICIALILIANJ
1. No-code machine learning allows users to build machine learning applications and tools through a drag-and-drop interface without coding, making ML more accessible.
2. Tiny ML focuses on applying machine learning at the edge on small IoT devices to reduce latency, bandwidth usage, and ensure privacy while still enabling useful predictions from collected data.
3. Automated machine learning aims to simplify the entire machine learning process from data preprocessing to modeling to reduce costs and expertise needed, enabling more widespread use of analytical tools and technologies.
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
This document summarizes a presentation on applied machine learning. It discusses using machine learning techniques like support vector machines (SVMs) and kernels to solve problems like spam classification, natural vs plastic apple detection, handwritten digit recognition, drug discovery, and gene finding. It provides examples of applications like analyzing source code and binary files, distinguishing Linux from OpenBSD, and detecting splice sites in genomic DNA. It also discusses ongoing research areas and capabilities of current machine learning techniques. Finally, it briefly outlines work using machine learning for brain-computer interfacing applications like solving the cocktail party problem using independent component analysis.
2. Mail.Ru have own Search Engine
– It is not «patched Google»
– Completely out product
– 8.3% of market
- The same engine for:
- web
- image
- video
- news
- real time
- e-mail search
- etc.
2
1
3. W
We love Machine Learning :)
Simplified Search Engine Architecture:
ML
Crawler – StaticRank: crawling priority
Web
ML
Analyzer – Antispam – Object extraction
– Antiporn – User behaviour
Indexer
ML
– Ranking
Searchers
– Lingustic
ML – Query processing
Frontend – Antirobot
3
– Decision when to show vertical search
2
4. What is Supervised Machine Learning?
«Machine learning is the hot new thing»
John Hennessy, President, Stanford
4
3
5. Supervised Machine Learning
There is unknown function
Given:
Set of samples
Goal:
Build approximation of using
5
4
7. Proper model selection
7
Image from: Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer. 2006
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8. Importance of train set construction
Usually:
- random sampling is not the best strategy
- labeling is very expensive 8
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9. Information about train set construction
Modern books about ML:
- Pattern Recognition and Machine Learning — 0
- Elements of Statistical Learning — 0
- An Introduction to Information Retrieval — 1 paragraph
Book about TS construction:
Burr Settles. Active learning.
- Publication Date: 2 July 2012
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10. Idea of Active Learning
Image from: Burr Settles. Active Learning Literature Survey. Computer
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Sciences Technical Report 1648, University of Wisconsin–Madison. 2009.
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11. Active learning workflow
Main assumption: obtaining an unlabeled instance is free
Image from: Burr Settles. Active Learning Literature Survey. Computer
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Sciences Technical Report 1648, University of Wisconsin–Madison. 2009.
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12. Sampling scenarios
Image from: Burr Settles. Active Learning Literature Survey. Computer
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Sciences Technical Report 1648, University of Wisconsin–Madison. 2009.
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13. Uncertainty Sampling
Take instances about which it is least certain how to label.
Least confident:
Margin sampling
Entropy
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18. Query-By-Committee (QBag)
Input: T – labeled train set
С – size of the committe
A – learning algorithm
U – set of unlabeled objects
1. Uniformly resample T, obtain T1...TС, where |Ti| < |T|
2. For each Ti build model Hi using A
3. Select x* = min x∈U | |Hi(x)=1| - |Hi(x)=0| |
4. Pass x* to oracle and update T
5. Repeat from 1until convergence
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19. QBag: Quality
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K.Dwyer, R.Holte, Decision Tree Instability and Active Learning, 2007
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20. QBag: Stability
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K.Dwyer, R.Holte, Decision Tree Instability and Active Learning, 2007
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22. Other methods
Expected Model Change
Expected Error Reduction
Variance Reduction
Many «model dependent» methods
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23. Example 1:
Recognition of sentence boundaries
«I recommend Mitchell (1997) or Duda et al. (2001). I have strived...»
Idea:
1. Make set of simple regexp-based rules like
«big letter at the right» or
«digits at the rigth and left»
(40 rules)
2. Build classifier:
Gradient Boosted Decision Trees
A.Kudinov, A.Voropaev, A.Kalinin. A HIGHLY PRECISIONAL METHOD FOR THE
RECOGNITION OF SENTENCE BOUNDARIES. Computational Linguistics and Intellectual
Technologies. 2011 23
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24. Example 1:
Recognition of sentence boundaries
OpenNLP / «AOT»
project
Mail.Ru
detector / Mail.Ru
Sentence Random detector /
Boundary sampling least
Detector / confident
Random
sampling
Error 41,2 % 30,4 % 8.2 % 0,8 %
Rate
Train set: 9820 examples
Validation: 500 examples 24
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25. Example 2:
Ranking formula
Idea:
1. Query-Document presented by x, where:
x1 – tf-idf rank
x2 – query geographical
x3 – geo-region of user is equal to document's region
(600 other factors)
2. Build train set:
5 – vital
4 – exact answer
3 – usefull
2 – slightly usefull
1 – out of topic
0 – can't be labeled (unknown language, etc.)
3. Train ranking formula using modified LambdaRank
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28. Example 2:
Density of QDocuments on the map
High density
Low density
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29. Example 2:
Result of “SOM miss” selection
Old documents
New documents
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30. Example 2:
Qbag + SOM heuristic
1. Transform regression to binary classification problem:
- We should order pair of QDocuments
- Apply QBag and select pairs of QDocuments
2. Apply SOM heuristic
- Construct initial trainset T
- Filter selected pairs
A) Don't take pair
B) May be
C) Take pair
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