Reading List

This repository collates the papers I am reading for my MSCS thesis.

Paper	Method	Venue	Code
Analyzing and Improving Representations with the Soft Nearest Neighbor Loss	SNNL	ICML 2019
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models	MMVAE	NeurIPS 2019	PyTorch
Multimodal Generative Models for Scalable Weakly-Supervised Learning	MVAE	NeurIPS 2018	PyTorch
Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization	SS AVTS	NeurIPS 2018
Cooperative neural networks: Exploiting prior independence structure for improved classification	CoNN	NeurIPS 2018	PyTorch
Learning to Specialize with Knowledge Distillation for Visual Question Answering	MCL-KD	NeurIPS 2018	PyTorch
KATE: K-Competitive Autoencoder for Text	KATE	KDD 2017	Keras
Distilling the Knowledge in Neural Networks	KD	NeurIPS 2015 Workshop
A network of deep neural networks for distant speech recognition	CN-DNN	ICASSP 2017
Cooperative Training for Generative Modeling of Discrete Data	CoT	ICML 2019
Learning a Text-Video Embedding from Incomplete and Heterogeneous Data	MEE		PyTorch
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings	MIX'EM	ACCV 2020
Confident Multiple Choice Learning	CMCL	ICML 2017	TensorFlow
Stochastic Multiple Choice Learning	sMCL	NeurIPS 2016

Paper	Method	Venue	Code
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs	MCL	NeurIPS 2012
A Cooperative Ensemble Learning System	CELS	IJCNN 1998

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