ML Sessions 2019

Unsupervised / Semi-Supervised Learning

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2019-10-24	Small Data Challenges in Big Data Era: Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
2019-11-07	Block Neural Autoregressive Flow
2019-11-21	Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
2019-12-05	Structured Disentangled Representations

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2019-05-02	Generalization in Deep Learning
2019-05-16	Understanding deep learning requires rethinking generalization
2019-06-13	Emergence of Invariance and Disentanglement in Deep Representations
2019-09-11	Three Factors Influencing Minima in SGD
2019-09-26	Exploring Generalization in Deep Learning
2019-10-10	On the importance of single directions for generalization Predicting the Generalization Gap in Deep Networks with Margin Distributions

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Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

An Introduction to MCMC for Machine Learning (second part plus coding)

fhttps://www.cs.ubc.ca/~arnaud/andrieu_defreitas_doucet_jordan_intromontecarlomachinelearning.pdf

An Introduction to MCMC for Machine Learning (part 1)

fhttps://www.cs.ubc.ca/~arnaud/andrieu_defreitas_doucet_jordan_intromontecarlomachinelearning.pdf

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e-SNLI: Natural Language Inference with Natural Language Explanations	Judith	Petra
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation	Sanne	Sarah
Methods for Interpreting and Understanding Deep Neural Networks	Federico	Matteo

Network Disection: Quantifying Interpretability of Deep Visual Representations	Louise
Gradcam++	Sofia	Marcus

A causal framework for explaining the predictions of black-box sequence-to-sequence models