Search Results for author:
Found 7 papers, 3 papers with code
Disentanglement Analysis with Partial Information Decomposition
We propose a framework to analyze how multivariate representations disentangle ground-truth generative factors.
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Software frameworks for neural networks play a key role in the development and application of deep learning methods.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Evaluating the Variance of Likelihood-Ratio Gradient Estimators
The framework gives a natural derivation of the optimal estimator that can be interpreted as a special case of the likelihood-ratio method so that we can evaluate the optimal degree of practical techniques with it.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Reparameterization trick for discrete variables
Low-variance gradient estimation is crucial for learning directed graphical models parameterized by neural networks, where the reparameterization trick is widely used for those with continuous variables.
Chainer: a Next-Generation Open Source Framework for Deep Learning
Software frameworks for neural networks play key roles in the development and application of deep learning methods.
