From e9757089ac4ecb78591be793d13e6d2c89b8fa72 Mon Sep 17 00:00:00 2001 From: ReDeiPirati Date: Wed, 29 Nov 2017 08:55:32 +0100 Subject: [PATCH] Add google SVCCA and Stanford Palliative Care DL --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index b65b113..7cf4aae 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ - CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning [[arXiv](https://arxiv.org/abs/1711.05225)] [[article](https://stanfordmlgroup.github.io/projects/chexnet/)] - Online Deep Learning: Learning Deep Neural Networks on the Fly [[arXiv](https://arxiv.org/abs/1711.03705)] - Learning Explanatory Rules from Noisy Data [[arXiv](https://arxiv.org/abs/1711.04574)] +- Improving Palliative Care with Deep Learning [[arXiv](https://arxiv.org/abs/1711.06402)] [[article](https://stanfordmlgroup.github.io/projects/improving-palliative-care/)] - Weighted Transformer Network for Machine Translation [[arXiv](https://arxiv.org/abs/1711.02132)] [[article](https://einstein.ai/research/weighted-transformer)] - Non-Autoregressive Neural Machine Translation [[arXiv](https://arxiv.org/abs/1711.02281)] [[article](https://einstein.ai/research/non-autoregressive-neural-machine-translation)] - Block-Sparse Recurrent Neural Networks [[arXiv](https://arxiv.org/abs/1711.02782)] @@ -142,6 +143,7 @@ Weakly-Supervised Classification and Localization of Common Thorax Diseases [[CV - Programmable Agents [[arXiv](https://arxiv.org/abs/1706.06383)] - Grounded Language Learning in a Simulated 3D World [[arXiv](https://arxiv.org/abs/1706.06551)] - Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics [[arXiv](https://arxiv.org/abs/1706.04317)] +- SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability [[arXiv](https://arxiv.org/abs/1706.05806)] [[article](https://research.googleblog.com/2017/11/interpreting-deep-neural-networks-with.html)] [[code](https://github.com/google/svcca)] - One Model To Learn Them All [[arXiv](https://arxiv.org/abs/1706.05137)] [[code](https://github.com/tensorflow/tensor2tensor)] [[article](https://research.googleblog.com/2017/06/multimodel-multi-task-machine-learning.html)] - Hybrid Reward Architecture for Reinforcement Learning [[arXiv](https://arxiv.org/abs/1706.04208)] - Expected Policy Gradients [[arXiv](https://arxiv.org/abs/1706.05374)]