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Gradient-based learning applied to document recognition
This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.
Wasserstein Generative Adversarial Networks
- Martín Arjovsky, Soumith Chintala, L. Bottou
- Computer ScienceInternational Conference on Machine Learning
- 17 July 2017
This work introduces a new algorithm named WGAN, an alternative to traditional GAN training that can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.
Natural Language Processing (Almost) from Scratch
- Ronan Collobert, J. Weston, L. Bottou, Michael Karlen, K. Kavukcuoglu, P. Kuksa
- Computer ScienceJournal of machine learning research
- 1 February 2011
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity…
Large-Scale Machine Learning with Stochastic Gradient Descent
- L. Bottou
- Computer ScienceInternational Conference on Computational…
A more precise analysis uncovers qualitatively different tradeoffs for the case of small-scale and large-scale learning problems.
Optimization Methods for Large-Scale Machine Learning
A major theme of this study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter, leading to a discussion about the next generation of optimization methods for large- scale machine learning.
Invariant Risk Minimization
This work introduces Invariant Risk Minimization, a learning paradigm to estimate invariant correlations across multiple training distributions and shows how the invariances learned by IRM relate to the causal structures governing the data and enable out-of-distribution generalization.
Signature Verification Using A "Siamese" Time Delay Neural Network
- J. Bromley, James W. Bentz, Roopak Shah
- Computer ScienceInternational journal of pattern recognition and…
- 1 August 1993
An algorithm for verification of signatures written on a pen-input tablet based on a novel, artificial neural network called a "Siamese" neural network, which consists of two identical sub-networks joined at their outputs.
Towards Principled Methods for Training Generative Adversarial Networks
The goal of this paper is to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks, and performs targeted experiments to substantiate the theoretical analysis and verify assumptions, illustrate claims, and quantify the phenomena.
Stochastic Gradient Descent Tricks
- L. Bottou
- Computer ScienceNeural Networks
This chapter provides background material, explains why SGD is a good learning algorithm when the training set is large, and provides useful recommendations.