Final project, Machine Learning Having it Deep and Structured, NTU
- Rank 1/25 in peer review, original score: 16.2/17
- 2nd presentation pride (voted by audience)
The document summarizes the Batch Normalization technique presented in the paper "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". Batch Normalization aims to address the issue of internal covariate shift in deep neural networks by normalizing layer inputs to have zero mean and unit variance. It works by computing normalization statistics for each mini-batch and applying them to the inputs. This helps in faster and more stable training of deep networks by reducing the distribution shift across layers. The paper presented ablation studies on MNIST and ImageNet datasets showing Batch Normalization improves training speed and accuracy compared to prior techniques.
Batch normalization is a technique introduced in 2015 by Google researchers to address issues like internal covariate shift and vanishing gradients. It works by normalizing the inputs to each unit to have zero mean and unit variance based on the statistics of the mini-batch. This helps the network train deeper models with higher learning rates and be less sensitive to initialization. Batch normalization is applied before the activation function of each layer during both training and inference.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Machine Learning - Convolutional Neural NetworkRichard Kuo
The document provides an overview of convolutional neural networks (CNNs) for visual recognition. It discusses the basic concepts of CNNs such as convolutional layers, activation functions, pooling layers, and network architectures. Examples of classic CNN architectures like LeNet-5 and AlexNet are presented. Modern architectures such as Inception and ResNet are also discussed. Code examples for image classification using TensorFlow, Keras, and Fastai are provided.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
The document summarizes the Batch Normalization technique presented in the paper "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift". Batch Normalization aims to address the issue of internal covariate shift in deep neural networks by normalizing layer inputs to have zero mean and unit variance. It works by computing normalization statistics for each mini-batch and applying them to the inputs. This helps in faster and more stable training of deep networks by reducing the distribution shift across layers. The paper presented ablation studies on MNIST and ImageNet datasets showing Batch Normalization improves training speed and accuracy compared to prior techniques.
Batch normalization is a technique introduced in 2015 by Google researchers to address issues like internal covariate shift and vanishing gradients. It works by normalizing the inputs to each unit to have zero mean and unit variance based on the statistics of the mini-batch. This helps the network train deeper models with higher learning rates and be less sensitive to initialization. Batch normalization is applied before the activation function of each layer during both training and inference.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Machine Learning - Convolutional Neural NetworkRichard Kuo
The document provides an overview of convolutional neural networks (CNNs) for visual recognition. It discusses the basic concepts of CNNs such as convolutional layers, activation functions, pooling layers, and network architectures. Examples of classic CNN architectures like LeNet-5 and AlexNet are presented. Modern architectures such as Inception and ResNet are also discussed. Code examples for image classification using TensorFlow, Keras, and Fastai are provided.
Recurrent Neural Networks have shown to be very powerful models as they can propagate context over several time steps. Due to this they can be applied effectively for addressing several problems in Natural Language Processing, such as Language Modelling, Tagging problems, Speech Recognition etc. In this presentation we introduce the basic RNN model and discuss the vanishing gradient problem. We describe LSTM (Long Short Term Memory) and Gated Recurrent Units (GRU). We also discuss Bidirectional RNN with an example. RNN architectures can be considered as deep learning systems where the number of time steps can be considered as the depth of the network. It is also possible to build the RNN with multiple hidden layers, each having recurrent connections from the previous time steps that represent the abstraction both in time and space.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
Recurrent neural networks (RNNs) are a type of artificial neural network that can process sequential data of varying lengths. Unlike traditional neural networks, RNNs maintain an internal state that allows them to exhibit dynamic temporal behavior. RNNs take the output from the previous step and feed it as input to the current step, making the network dependent on information from earlier steps. This makes RNNs well-suited for applications like text generation, machine translation, image captioning, and more. RNNs can remember information for long periods of time but are difficult to train due to issues like vanishing gradients.
Introduction to Recurrent Neural NetworkKnoldus Inc.
The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
The document discusses the BERT model for natural language processing. It begins with an introduction to BERT and how it achieved state-of-the-art results on 11 NLP tasks in 2018. The document then covers related work on language representation models including ELMo and GPT. It describes the key aspects of the BERT model, including its bidirectional Transformer architecture, pre-training using masked language modeling and next sentence prediction, and fine-tuning for downstream tasks. Experimental results are presented showing BERT outperforming previous models on the GLUE benchmark, SQuAD 1.1, SQuAD 2.0, and SWAG. Ablation studies examine the importance of the pre-training tasks and the effect of model size.
AlexNet achieved unprecedented results on the ImageNet dataset by using a deep convolutional neural network with over 60 million parameters. It achieved top-1 and top-5 error rates of 37.5% and 17.0%, significantly outperforming previous methods. The network architecture included 5 convolutional layers, some with max pooling, and 3 fully-connected layers. Key aspects were the use of ReLU activations for faster training, dropout to reduce overfitting, and parallelizing computations across two GPUs. This dramatic improvement demonstrated the potential of deep learning for computer vision tasks.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
Link prediction 방법의 개념 및 활용Kyunghoon Kim
The document discusses link prediction in social networks. It begins with an introduction to social networks and link prediction. It then covers the framework of link prediction, including common methods and applications. As an example, it discusses using link prediction to analyze terrorist networks. Finally, it discusses performing link prediction using Python tools like NumPy, Pandas, and NetworkX.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
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.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
Deep Learning - Overview of my work IIMohamed Loey
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
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.
Batch normalization is a technique that standardizes the inputs to each node in a neural network. It reduces the problem of internal covariate shift, where the distribution of inputs changes as the network trains, slowing progress. The technique normalizes each input by subtracting the batch mean and dividing by the batch standard deviation. It also learns scale and shift parameters to scale the normalized values. This technique speeds up training by reducing internal covariate shift and making training more robust to network initialization.
4 high performance large-scale image recognition without normalizationDonghoon Park
1) A new state-of-the-art image recognition model achieves 86.5% top-1 accuracy on ImageNet without using batch normalization, which is typically used for training deep neural networks.
2) The proposed method uses adaptive gradient clipping to stabilize training without normalization. This allows training with larger batch sizes while maintaining performance compared to batch-normalized models.
3) Experimental results show the proposed normalizer-free models match or exceed the performance of equivalent batch-normalized models while training faster.
The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.
Recurrent neural networks (RNNs) are a type of artificial neural network that can process sequential data of varying lengths. Unlike traditional neural networks, RNNs maintain an internal state that allows them to exhibit dynamic temporal behavior. RNNs take the output from the previous step and feed it as input to the current step, making the network dependent on information from earlier steps. This makes RNNs well-suited for applications like text generation, machine translation, image captioning, and more. RNNs can remember information for long periods of time but are difficult to train due to issues like vanishing gradients.
Introduction to Recurrent Neural NetworkKnoldus Inc.
The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.
The document discusses the BERT model for natural language processing. It begins with an introduction to BERT and how it achieved state-of-the-art results on 11 NLP tasks in 2018. The document then covers related work on language representation models including ELMo and GPT. It describes the key aspects of the BERT model, including its bidirectional Transformer architecture, pre-training using masked language modeling and next sentence prediction, and fine-tuning for downstream tasks. Experimental results are presented showing BERT outperforming previous models on the GLUE benchmark, SQuAD 1.1, SQuAD 2.0, and SWAG. Ablation studies examine the importance of the pre-training tasks and the effect of model size.
AlexNet achieved unprecedented results on the ImageNet dataset by using a deep convolutional neural network with over 60 million parameters. It achieved top-1 and top-5 error rates of 37.5% and 17.0%, significantly outperforming previous methods. The network architecture included 5 convolutional layers, some with max pooling, and 3 fully-connected layers. Key aspects were the use of ReLU activations for faster training, dropout to reduce overfitting, and parallelizing computations across two GPUs. This dramatic improvement demonstrated the potential of deep learning for computer vision tasks.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
Link prediction 방법의 개념 및 활용Kyunghoon Kim
The document discusses link prediction in social networks. It begins with an introduction to social networks and link prediction. It then covers the framework of link prediction, including common methods and applications. As an example, it discusses using link prediction to analyze terrorist networks. Finally, it discusses performing link prediction using Python tools like NumPy, Pandas, and NetworkX.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This Edureka Recurrent Neural Networks tutorial will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
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.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
Deep Learning: Recurrent Neural Network (Chapter 10) Larry Guo
This Material is an in_depth study report of Recurrent Neural Network (RNN)
Material mainly from Deep Learning Book Bible, http://www.deeplearningbook.org/
Topics: Briefing, Theory Proof, Variation, Gated RNNN Intuition. Real World Application
Application (CNN+RNN on SVHN)
Also a video (In Chinese)
https://www.youtube.com/watch?v=p6xzPqRd46w
In this presentation we discuss the convolution operation, the architecture of a convolution neural network, different layers such as pooling etc. This presentation draws heavily from A Karpathy's Stanford Course CS 231n
Deep Learning - Overview of my work IIMohamed Loey
Deep Learning Machine Learning MNIST CIFAR 10 Residual Network AlexNet VGGNet GoogleNet Nvidia Deep learning (DL) is a hierarchical structure network which through simulates the human brain’s structure to extract the internal and external input data’s features
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.
Batch normalization is a technique that standardizes the inputs to each node in a neural network. It reduces the problem of internal covariate shift, where the distribution of inputs changes as the network trains, slowing progress. The technique normalizes each input by subtracting the batch mean and dividing by the batch standard deviation. It also learns scale and shift parameters to scale the normalized values. This technique speeds up training by reducing internal covariate shift and making training more robust to network initialization.
4 high performance large-scale image recognition without normalizationDonghoon Park
1) A new state-of-the-art image recognition model achieves 86.5% top-1 accuracy on ImageNet without using batch normalization, which is typically used for training deep neural networks.
2) The proposed method uses adaptive gradient clipping to stabilize training without normalization. This allows training with larger batch sizes while maintaining performance compared to batch-normalized models.
3) Experimental results show the proposed normalizer-free models match or exceed the performance of equivalent batch-normalized models while training faster.
This paper proposes a method called network deconvolution to remove pixel-wise and channel-wise correlation in convolutional networks. It does this by learning a decorrelation matrix during training that whitens the input data, removing redundancy. Experiments show it converges faster than batch normalization and achieves better performance on image classification tasks. The method is inspired by the decorrelation process observed in animal visual cortex and results in sparser representations.
This document provides a practical guide for using support vector machines (SVMs) for classification tasks. It recommends beginners follow a simple procedure: 1) preprocess data by converting categorical features to numeric and scaling attributes, 2) use a radial basis function kernel, 3) perform cross-validation to select optimal values for hyperparameters C and γ, and 4) train the full model on the training set using the best hyperparameters. The guide explains why this procedure often provides reasonable results for novices and illustrates it using examples of real-world classification problems.
This document proposes a simple procedure for beginners to obtain reasonable results when using support vector machines (SVMs) for classification tasks. The procedure involves preprocessing data through scaling, using a radial basis function kernel, selecting model parameters through cross-validation grid search, and training the full model on the preprocessed data. The document provides examples applying this procedure to real-world datasets, demonstrating improved accuracy over approaches without careful preprocessing and parameter selection.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
Generating super resolution images using transformersNEERAJ BAGHEL
The document summarizes a research paper on using transformers for the task of natural language processing. Some key points:
- Transformers use attention mechanisms to draw global dependencies between input and output without regard to sequence length, addressing limitations of RNNs and CNNs for NLP tasks.
- The proposed transformer architecture contains self-attention layers in the encoder and decoder, as well as an attention mechanism between the encoder and decoder.
- The transformer uses scaled dot-product attention and multi-head attention. Self-attention allows relating different positions of a single sequence to compute representations.
- Other components include feedforward layers and positional encoding to inject information about the relative or absolute positions of the tokens in the sequence
This document discusses training deep neural network (DNN) models. It explains that DNNs have an input layer, multiple hidden layers, and an output layer connected by weights and biases. Training a DNN involves initializing the weights and biases randomly, passing inputs through the network to get outputs, calculating the loss between actual and predicted outputs, and updating the weights to minimize loss using gradient descent and backpropagation. Gradient descent with backpropagation calculates the gradient of the loss with respect to each weight and bias by applying the chain rule to propagate loss backwards through the network.
ImageNet classification with deep convolutional neural networks(2012)WoochulShin10
1) The document describes a study that trained one of the largest convolutional neural networks on the ImageNet dataset.
2) It implemented highly optimized GPU training of large CNNs on high resolution images and introduced features like ReLU, local response normalization, and overlapping pooling to improve performance and reduce overfitting.
3) The network architecture consisted of 5 convolutional layers and 3 fully-connected layers and was trained on two GPUs with techniques like dropout and data augmentation to reduce overfitting.
Objective Evaluation of a Deep Neural Network Approach for Single-Channel Spe...csandit
Single-channel speech intelligibility enhancement is much more difficult than multi-channel
intelligibility enhancement. It has recently been reported that machine learning training-based
single-channel speech intelligibility enhancement algorithms perform better than traditional
algorithms. In this paper, the performance of a deep neural network method using a multiresolution
cochlea-gram feature set recently proposed to perform single-channel speech
intelligibility enhancement processing is evaluated. Various conditions such as different
speakers for training and testing as well as different noise conditions are tested. Simulations
and objective test results show that the method performs better than another deep neural
networks setup recently proposed for the same task, and leads to a more robust convergence
compared to a recently proposed Gaussian mixture model approach.
The document summarizes Yan Xu's upcoming presentation at the Houston Machine Learning Meetup on dimension reduction techniques. Yan will cover linear methods like PCA and nonlinear methods such as ISOMAP, LLE, and t-SNE. She will explain how these methods work, including preserving variance with PCA, using geodesic distances with ISOMAP, and modeling local neighborhoods with LLE and t-SNE. Yan will also demonstrate these methods on a dataset of handwritten digits. The meetup is part of a broader roadmap of machine learning topics that will be covered in future sessions.
Decision Forests and discriminant analysispotaters
This document summarizes a tutorial on randomised decision forests and tree-structured algorithms. It discusses how tree-based algorithms like boosting and random forests can be used for tasks like object detection, tracking and segmentation. It also describes techniques for speeding up computation, such as converting boosted classifiers to decision trees and using multiple classifier systems. The tutorial is structured in two parts, covering tree-structured algorithms and randomised forests.
Batch normalization: Accelerating Deep Network Training by Reducing Internal ...ssuser6a46522
Batch normalization is a technique that normalizes the inputs to each node in a neural network layer. It reduces internal covariate shift and allows higher learning rates to be used, speeding up training. The method normalizes each feature by subtracting the batch mean and dividing by the batch standard deviation. It was shown to significantly accelerate training of state-of-the-art models on MNIST and ImageNet, requiring only a fraction of the training steps to reach the same level of accuracy compared to models without batch normalization.
The slides for the techniques used in the Temporal Segment Network (TSN), including the basic ideas, recall of BN-Inception, optical flow and tricks in application. Used in group paper reading in University of Sydney.
Similar to Why Batch Normalization Works so Well (20)
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
The binding of cosmological structures by massless topological defectsSérgio Sacani
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
1. Why Batch Normalization
Works so Well
Group:We are the REAL baseline
D05921027 Chun-Min Chang, D05921018 Chia-Ching Lin
F03942038 Chia-Hao Chung, R05942102 Kuan-Hua Wang
2. Internal Covariate Shift
• During training, layers need to continuously adapt to the new
distribution of their inputs
w
𝑧↓
1
𝑧↓
2
𝑧
w
3. Batch Normalization (BN)
• Goal: to speed up the process of training deep neural networks by
reducing internal covariate shift
𝑧↓
1
𝑧↓
2
𝑧↑′
ww
𝑧↓
1
↑′
𝑧↓
2
BN
BN
4. Idea of BN
• Full whitening? Too costly!
• 2 necessary simplifications
a. Normalize each feature dimention (no decorrelation)
b. Normalize each batch
• E.g., for the 𝑘-dim input vector:
• Also, “scale” and “shift” parameters are introduced to preserve network
capacity
batch mean
batch variance
𝑥 ↑( 𝑘) = 𝑥↑( 𝑘) −E[ 𝑥↑( 𝑘) ]/√Var[ 𝑥↑( 𝑘) ]
𝑦↑( 𝑘) = 𝛾↑( 𝑘) 𝑥 ↑( 𝑘) + 𝛽↑( 𝑘)
5. BN Algorithm (1/2)
• Training:
𝜖 is a constant preventing
division by zero, e.g., 0.001
6. BN Algorithm (2/2)
• Testing: use population statistics ( 𝜇 and 𝜎) estimated using moving averages
of batch statistics ( 𝜇↓𝐵 and 𝜎↓𝐵 ) during training
𝛼 is the moving average momentum, e.g., 0.999
𝜇← 𝛼𝜇+(1− 𝛼) 𝜇↓B
𝜎← 𝛼𝜎+(1− 𝛼) 𝜎↓B
7. Problems of Interest
• To understand the effect of BN w.r.t. the following network components
(1) activation function
(2) optimizer
(3) batch size
(4) training/testing data distribution
• To validate the claims in the original BN paper
(5) BN solves the issue of gradient vanishing
(6) BN regularizes the model
(7) BN helps making singular values of layers’ Jacobian closer to 1
• (8) To compare BN with batch renormalization (BRN)
8. Experiment Setup
• Toolkit: tensorflow
• Dataset: MNIST
• Network structure: DNN of 2 hidden layers, both 100 neurons
• Default parameters (may change for different experiments)
(1) learning rate: 0.0001
(2) batch size: 64
(3) activation function: sigmoid
(4) optimizer: SGD
• BNs are implemented before activation functions
9. To understand the effect of BN w.r.t. the
following network components
(1) activation function
(2) optimizer
(3) batch size
(4) training/testing data distribution
10. (1) Activation Function
• In all cases, BN significantly
improves the speed of
training
• Sigmoid w/o BN: gradient
vanishing
11. (2) Optimizer
• ReLU+Adam ≈ ReLU+SGD
+BN
(same as Sigmoid)
with BN, the selection of
optimizers does not lead to
significant difference
12. (3) Batch Size
• For small batch size (i.e., 4), BN
degrades the performance
13. (4) Mismatch between Training and Testing
• For binary classification task with extremely imbalanced testing distribution
(e.g., 99 : 1), it is no surprise that BN ruins the performance
14. Brief Summary I
1. BN speeds up training process and improves performance for all choices of activation
functions and optimizers, with the biggest improvement when Sigmoid is used
2. For BN, the choice of activation functions is more crucial than the choice of optimizer
3. BN worsens performance if (1) too small batch size, or (2) greatly mismatched
training/testing data distribution
15. To validate the claims in the BN paper
(5) BN solves the issue of gradient vanishing
(6) BN regularizes the model
(7) BN helps making singular values of layers’
Jacobian closer to 1
16. (5) BN does solve the issue of gradient vanishing
0.02
0.10
5x
Without BN
0.10
0.15
With BN
0.20
0.20
Layer 1
Layer 2
Layer 1
Layer 2
Sigmoid ReLU
17. (6) BN does regularize the model
• E.g., average magnitude of weights in layer 2
w11
w12
w22
w21
BN
BN
×
𝛾↑1
𝑎
↓
1
↑
1
𝑎
↓
2
↑
1
𝑎
↓
2
↑
2
𝑎
↓
1
↑
2
+
𝛽↑1
×
𝛾↑2 +
𝛽↑2
w’s can be smaller since we have 𝛾’s
18. Does BN benefit the gradient flow?
• Isometry (保距轉換):
è singular values are closed to 1
• Recall that errors are back-propagated via layer Jacobian matrix
• Claim: BN can help making singular values of layers’ Jacobian closer to 1
25. Brief Summary II
1. BN does solve the issue of gradient vanishing
2. BN does regularize the weights
3. BN does benefit the gradient flow by making singular values of layers’
Jacobian closer to 1
26. To compare BN with batch
renormalization (BRN)
(8) Does BRN really solve the problems of BN?
27. Batch Renormalization (BRN)
• Recall that BN worsens performance if (1) too small batch size, or (2) greatly
mismatched training/testing data distribution
• This is mainly due to the mismatch between batch statistics (used during
training) and estimated population statistics (used during testing)
• BRN introduces two parameters 𝑟 and 𝑑 to fix this mismatch:
BN
BRN
28. BRN Algorithm
• During training, population statistics are
maintained and introduced in normalization
process
• During testing, estimated population
statistics are used
Note that when 𝑟=1 and 𝑑=0, BRN = BN
29. BN vs. BRN under small batch size
• BRN survives under small batch size: 4
30. Conclusions
We have showed experimentally that
1. BN speeds up training process and improves performance no matter which
activation functions or optimizers are used
。With BN, activation function is more crucial than optimizer
2. BN does…
(1) solve the issue of gradient vanishing
(2) regularize the weights
(3) benefit gradient flow through network
3. BN worsens performance if (1) too small batch size, or (2) greatly
mismatched training/testing data distribution
è Solved by BRN
31. References
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32. References
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networks with natural gradient and parameter averaging. CoRR, abs/1410.7455, 2014.
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(2016, June). Analysis of Deep Neural Networks with the Extended Data Jacobian Matrix. In Proceedings of The 33rd
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https://r2rt.com/implementing-batch-normalization-in-tensorflow.html