Search Results for author: Yuxin Wu

Found 14 papers, 11 papers with code

Rethinking "Batch" in BatchNorm

1 code implementation17 May 2021 Yuxin Wu, Justin Johnson

BatchNorm is a critical building block in modern convolutional neural networks.

Bayesian Relational Memory for Semantic Visual Navigation

1 code implementation ICCV 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards.

Visual Navigation

Natural Environment Benchmarks for Reinforcement Learning

1 code implementation14 Nov 2018 Amy Zhang, Yuxin Wu, Joelle Pineau

While current benchmark reinforcement learning (RL) tasks have been useful to drive progress in the field, they are in many ways poor substitutes for learning with real-world data.

Learning and Planning with a Semantic Model

no code implementations ICLR 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.

Visual Navigation

Group Normalization

18 code implementations ECCV 2018 Yuxin Wu, Kaiming He

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Object Detection Video Classification

Building Generalizable Agents with a Realistic and Rich 3D Environment

5 code implementations ICLR 2018 Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian

To generalize to unseen environments, an agent needs to be robust to low-level variations (e. g. color, texture, object changes), and also high-level variations (e. g. layout changes of the environment).

Data Augmentation

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

2 code implementations NeurIPS 2017 Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick

In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.

Atari Games Starcraft

Effective Quantization Methods for Recurrent Neural Networks

2 code implementations30 Nov 2016 Qinyao He, He Wen, Shuchang Zhou, Yuxin Wu, Cong Yao, Xinyu Zhou, Yuheng Zou

In addition, we propose balanced quantization methods for weights to further reduce performance degradation.

Quantization

DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

12 code implementations20 Jun 2016 Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, Yuheng Zou

We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.

Quantization

Exploiting Local Structures with the Kronecker Layer in Convolutional Networks

no code implementations31 Dec 2015 Shuchang Zhou, Jia-Nan Wu, Yuxin Wu, Xinyu Zhou

In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.

Scene Text Recognition

Multilinear Map Layer: Prediction Regularization by Structural Constraint

no code implementations30 Jul 2015 Shuchang Zhou, Yuxin Wu

In this paper we propose and study a technique to impose structural constraints on the output of a neural network, which can reduce amount of computation and number of parameters besides improving prediction accuracy when the output is known to approximately conform to the low-rankness prior.

Cannot find the paper you are looking for? You can Submit a new open access paper.