The Importance of OSS Institutional Repository to ?Arabic Universities: Arabic DSpace 5 as a model - from an Arabic, Scientific Content Creation/Enrichment and Easy Access to Knowledge Prospective
The Importance of OSS Institutional Repository to ?Arabic Universities: Arabic DSpace 5 as a model - from an Arabic, Scientific Content Creation/Enrichment and Easy Access to Knowledge Prospective
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
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1) Start with a small learning rate and large batch size to find a flat minimum with good generalization. 2) Gradually increase the learning rate and decrease the batch size to find sharper minima that may improve training accuracy. 3) Monitor both training and validation/test accuracy - similar accuracy suggests good generalization while different accuracy indicates overfitting.
Modeling the Dynamics of SGD by Stochastic Differential EquationMark Chang
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The document discusses modeling stochastic gradient descent (SGD) using stochastic differential equations (SDEs). It outlines SGD, random walks, Wiener processes, and SDEs. It then covers continuous-time SGD and controlled SGD, modeling SGD as an SDE. It provides an example of modeling quadratic loss functions with SGD as an SDE. Finally, it discusses the effects of learning rate and batch size on generalization when modeling SGD as an SDE.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
The document discusses information theory concepts like entropy, joint entropy, conditional entropy, and mutual information. It then discusses how these concepts relate to generalization in deep learning models. Specifically, it explains that the PAC-Bayesian bound is data-dependent, so models with high VC dimension can still generalize if the data is clean, resulting in low KL divergence between the prior and posterior distributions.
The document outlines the PAC-Bayesian bound for deep learning. It discusses how the PAC-Bayesian bound provides a generalization guarantee that depends on the KL divergence between the prior and posterior distributions over hypotheses. This allows the bound to account for factors like model complexity and noise in the training data, avoiding some limitations of other generalization bounds. The document also explains how the PAC-Bayesian bound can be applied to stochastic neural networks by placing distributions over the network weights.
1) The document outlines PAC-Bayesian bounds, which provide probabilistic guarantees on the generalization error of a learning algorithm.
2) PAC-Bayesian bounds relate the expected generalization error of the output distribution Q to the training error, number of samples, and KL divergence between the prior P and posterior Q distributions over hypotheses.
3) The bounds show that better generalization requires a smaller divergence between P and Q, meaning the training process should not alter the distribution of hypotheses too much. This provides insights into reducing overfitting in deep learning models.
The document outlines the theory of domain adaptation. It discusses how the generalization bound from learning in a single domain does not apply when testing on a different target domain. The key challenges are the distance between the source and target features and the distance between their labeling functions. Domain adaptation aims to reduce these distances and provide a generalization bound by estimating these distances using a hypothesis trained on samples from both domains. An example approach is to find the hypothesis that minimizes the sum of source and target errors.
This document provides an overview of TensorFlow and how to implement machine learning models using TensorFlow. It discusses:
1) How to install TensorFlow either directly or within a virtual environment.
2) The key concepts of TensorFlow including computational graphs, sessions, placeholders, variables and how they are used to define and run computations.
3) An example one-layer perceptron model for MNIST image classification to demonstrate these concepts in action.
NTHU AI Reading Group: Improved Training of Wasserstein GANsMark Chang
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This document summarizes an NTHU AI Reading Group presentation on improved training of Wasserstein GANs. The presentation covered Wasserstein GANs, the derivation of the Kantorovich-Rubinstein duality, difficulties with weight clipping in WGANs, and a proposed gradient penalty method. It also outlined experiments on architecture robustness using LSUN bedrooms and character-level language modeling.
The document discusses the genome assembly problem which involves reconstructing the full genome sequence from fragmented short reads. It describes how short reads are fragmented and sequenced from the genome. To solve this problem, overlapping short reads must be found which is challenging with millions of reads. The document then explains how de Bruijn graphs can be used to represent overlaps between short reads by converting them to k-mers and building a graph from the k-mers to traverse and reconstruct the full genome sequence.
DRAW is a recurrent neural network proposed by Google DeepMind for image generation. It works by reconstructing images "step-by-step" through iterative applications of selective attention. At each step, DRAW samples from a latent space to generate values for its canvas. It uses an encoder-decoder RNN architecture with selective attention to focus on different regions of the image. This allows it to capture fine-grained details across the entire image.
This document discusses computer vision applications using TensorFlow for deep learning. It introduces computer vision and convolutional neural networks. It then demonstrates how to build and train a CNN for MNIST handwritten digit recognition using TensorFlow. Finally, it shows how to load and run the pre-trained Google Inception model for image classification.
This document discusses natural language processing applications using TensorFlow. It introduces natural language processing and the Word2vec neural network model. It then demonstrates an implementation of semantic operations using Word2vec embeddings trained on sample text data. Key steps include preprocessing the text, defining the computational graph in TensorFlow to train the Word2vec model, and obtaining the final word embeddings.
This document provides an introduction and overview of machine learning and TensorFlow. It discusses the different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It then explains concepts like logistic regression, softmax, and cross entropy that are used in neural networks. It covers how to evaluate models using metrics like accuracy, precision, and recall. Finally, it introduces TensorFlow as an open source machine learning framework and discusses computational graphs, automatic differentiation, and running models on CPU or GPU.
This document summarizes key concepts in neural sequence modeling including recurrent neural networks, long short-term memory networks, and neural Turing machines. It outlines recurrent neural networks and how they can be used for sequence modeling. It then describes long short-term memory networks and how they address the vanishing gradient problem in recurrent neural networks using gating mechanisms. Finally, it provides an overview of neural Turing machines and how they use an external memory component with addressing and reading/writing mechanisms controlled by a neural network controller.