Search Results for author: Zeming Lin

Found 14 papers, 10 papers with code

Neural Potts Model

no code implementations1 Jan 2021 Tom Sercu, Robert Verkuil, Joshua Meier, Brandon Amos, Zeming Lin, Caroline Chen, Jason Liu, Yann Lecun, Alexander Rives

We propose the Neural Potts Model objective as an amortized optimization problem.

Growing Action Spaces

1 code implementation ICML 2020 Gregory Farquhar, Laura Gustafson, Zeming Lin, Shimon Whiteson, Nicolas Usunier, Gabriel Synnaeve

In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.

reinforcement-learning Starcraft

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

1 code implementation ICLR 2018 Gabriel Synnaeve, Zeming Lin, Jonas Gehring, Dan Gant, Vegard Mella, Vasil Khalidov, Nicolas Carion, Nicolas Usunier

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games.


Value Propagation Networks

no code implementations ICLR 2018 Nantas Nardelli, Gabriel Synnaeve, Zeming Lin, Pushmeet Kohli, Philip H. S. Torr, Nicolas Usunier

We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments.

reinforcement-learning Starcraft

Automatic Differentiation in PyTorch

1 code implementation NIPS 2017 2017 Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, Adam Lerer

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.

Dimensionality Reduction General Classification

STARDATA: A StarCraft AI Research Dataset

1 code implementation7 Aug 2017 Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve

We provide full game state data along with the original replays that can be viewed in StarCraft.

Imitation Learning Starcraft

Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

3 code implementations ICLR 2018 Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov, Gabriel Synnaeve, Arthur Szlam, Rob Fergus

When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.

DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples

no code implementations22 Feb 2017 Ji Gao, Beilun Wang, Zeming Lin, Weilin Xu, Yanjun Qi

By identifying and removing unnecessary features in a DNN model, DeepCloak limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs.

General Classification

TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games

2 code implementations1 Nov 2016 Gabriel Synnaeve, Nantas Nardelli, Alex Auvolat, Soumith Chintala, Timothée Lacroix, Zeming Lin, Florian Richoux, Nicolas Usunier

We present TorchCraft, a library that enables deep learning research on Real-Time Strategy (RTS) games such as StarCraft: Brood War, by making it easier to control these games from a machine learning framework, here Torch.


MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

3 code implementations10 May 2016 Zeming Lin, Jack Lanchantin, Yanjun Qi

Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics.

General Classification Image Classification +1

Deep Motif: Visualizing Genomic Sequence Classifications

3 code implementations4 May 2016 Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi

This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task.

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