This Beautiful Fluid Simulator Warps Time…Kind Of 🌊

https://www.youtube.com/watch?v=wsFgrzYwchQ

📝 The paper "A Temporally Adaptive Material Point Method with Regional Time Stepping" is available here:

https://taichi.graphics/wp-content/uploads/2018/06/asyncmpm.pdf**

»My Top 10 Deep RL Papers of 2019»

https://roberttlange.github.io/posts/2019/12/blog-post-9/

DDSP: Differentiable Digital Signal Processing

DDSP is a library of differentiable versions of common DSP functions (such as synthesizers, waveshapers, and filters). This allows these interpretable elements to be used as part of an deep learning model, especially as the output layers for audio generation.

Code: https://github.com/magenta/ddsp

Paper: https://arxiv.org/abs/2001.04643v1

Advbox: a toolbox to generate adversarial examples that fool neural networks

Code: https://github.com/advboxes/AdvBox

Paper: https://arxiv.org/abs/2001.05574v1

The Two-Pass Softmax Algorithm

Code: https://github.com/google/XNNPACK

Paper: https://arxiv.org/abs/2001.04438v1

Controlling Text Generation with Plug and Play Language Models

https://eng.uber.com/pplm/

Paper: https://arxiv.org/abs/1912.02164

Code: https://github.com/uber-research/PPLM

The Autonomous Learning Library: A PyTorch Library for Building Reinforcement Learning Agents

https://autonomous-learning-library.readthedocs.io.

Code: https://github.com/cpnota/autonomous-learning-library

The Story of Heads

Code: https://github.com/lena-voita/the-story-of-heads

https://lena-voita.github.io/posts/acl19_heads.html



Paper: https://www.aclweb.org/anthology/P19-1580/

Deep Graph Matching Consensus

Code: https://github.com/rusty1s/deep-graph-matching-consensus

Paper: https://arxiv.org/abs/2001.09621v1

Spinning Up in Deep RL

https://spinningup.openai.com/en/latest/

Github: https://github.com/openai/spinningup

Encode, Tag and Realize: A Controllable and Efficient Approach for Text Generation

https://ai.googleblog.com/2020/01/encode-tag-and-realize-controllable-and.html

Code: https://github.com/google-research/lasertagger

Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

Code: https://github.com/cambridgeltl/parameter-factorization

Paper: https://arxiv.org/pdf/2001.11453.pdf

End-to-end training of sparse deep neural networks with little-to-no performance loss.

Code: https://github.com/google-research/rigl

Paper: https://arxiv.org/abs/1911.11134v1

Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network

Paper: https://arxiv.org/abs/2002.00544v1

Code: https://github.com/uwjunqi/Tensor-Train-Neural-Network