Simple & Effective Dimensionality Reduction for Word Embeddings. Presented at NIPS 2017 LLLD & RepL4NLP 2019 Workshop.
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Updated
May 7, 2020 - Python
Simple & Effective Dimensionality Reduction for Word Embeddings. Presented at NIPS 2017 LLLD & RepL4NLP 2019 Workshop.
Z-Forcing: Training Stochastic Recurrent Networks for Speech Modelling
Tensorflow Implementation of adversarial learning based adversarial example generator
Fast-Slow Recurrent Neural Networks
Reproduction code for WGAN-LP
Chainer implementation of the paper "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results" (https://arxiv.org/abs/1703.01780)
Tensorflow Implementation of "Large-scale Optimal Transport and Mapping Estimation"(ICLR2018/NIPS 2017 OTML)
Code for NIPS 2017 learning to run challenge
Implementation of the paper : "Toward Multimodal Image-to-Image Translation"
text convolution-deconvolution auto-encoder model in PyTorch
Our NIPS 2017: Learning to Run source code
PyTorch re-implementation of parts of "Deep Sets" (NIPS 2017)
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
Convolution dictionary learning for time-series
Implementation for <Deep Hyperspherical Learning> in NIPS'17.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
Code for "Effective Dimensionality Reduction for Word Embeddings".
Code/Model release for NIPS 2017 paper "Attentional Pooling for Action Recognition"
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