Search Results for author: Tong Che

Found 21 papers, 3 papers with code

Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

no code implementations18 May 2023 Wenhao Ding, Tong Che, Ding Zhao, Marco Pavone

Recently, reward-conditioned reinforcement learning (RCRL) has gained popularity due to its simplicity, flexibility, and off-policy nature.

Offline RL reinforcement-learning

SPE: Symmetrical Prompt Enhancement for Fact Probing

no code implementations14 Nov 2022 Yiyuan Li, Tong Che, Yezhen Wang, Zhengbao Jiang, Caiming Xiong, Snigdha Chaturvedi

In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction.

Guided Conditional Diffusion for Controllable Traffic Simulation

no code implementations31 Oct 2022 Ziyuan Zhong, Davis Rempe, Danfei Xu, Yuxiao Chen, Sushant Veer, Tong Che, Baishakhi Ray, Marco Pavone

Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles.

Autonomous Vehicles

Sparse Mixture-of-Experts are Domain Generalizable Learners

1 code implementation8 Jun 2022 Bo Li, Yifei Shen, Jingkang Yang, Yezhen Wang, Jiawei Ren, Tong Che, Jun Zhang, Ziwei Liu

It is motivated by an empirical finding that transformer-based models trained with empirical risk minimization (ERM) outperform CNN-based models employing state-of-the-art (SOTA) DG algorithms on multiple DG datasets.

Ranked #9 on Domain Generalization on DomainNet (using extra training data)

Domain Generalization Object Recognition

Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations

no code implementations29 Sep 2021 Xiao Jing, Zhenwei Zhu, Hongliang Li, Xin Pei, Yoshua Bengio, Tong Che, Hongyong Song

One of the greatest challenges of reinforcement learning is efficient exploration, especially when training signals are sparse or deceptive.

Distributed Computing Efficient Exploration

Energy-Based Open-World Uncertainty Modeling for Confidence Calibration

no code implementations ICCV 2021 Yezhen Wang, Bo Li, Tong Che, Kaiyang Zhou, Ziwei Liu, Dongsheng Li

Confidence calibration is of great importance to the reliability of decisions made by machine learning systems.

Rethinking Distributional Matching Based Domain Adaptation

no code implementations23 Jun 2020 Bo Li, Yezhen Wang, Tong Che, Shanghang Zhang, Sicheng Zhao, Pengfei Xu, Wei Zhou, Yoshua Bengio, Kurt Keutzer

In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods.

Domain Adaptation

Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

3 code implementations NeurIPS 2020 Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

To make that practical, we show that sampling from this modified density can be achieved by sampling in latent space according to an energy-based model induced by the sum of the latent prior log-density and the discriminator output score.

Image Generation

Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

no code implementations18 Nov 2019 Tong Che, Xiaofeng Liu, Site Li, Yubin Ge, Ruixiang Zhang, Caiming Xiong, Yoshua Bengio

We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.

Anomaly Detection Autonomous Driving +3

Conservative Wasserstein Training for Pose Estimation

no code implementations ICCV 2019 Xiaofeng Liu, Yang Zou, Tong Che, Peng Ding, Ping Jia, Jane You, Kumar B. V. K

We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.

Pose Estimation

Combining Model-based and Model-free RL via Multi-step Control Variates

no code implementations ICLR 2018 Tong Che, Yuchen Lu, George Tucker, Surya Bhupatiraju, Shane Gu, Sergey Levine, Yoshua Bengio

Model-free deep reinforcement learning algorithms are able to successfully solve a wide range of continuous control tasks, but typically require many on-policy samples to achieve good performance.

Continuous Control OpenAI Gym

Residual Connections Encourage Iterative Inference

no code implementations ICLR 2018 Stanisław Jastrzębski, Devansh Arpit, Nicolas Ballas, Vikas Verma, Tong Che, Yoshua Bengio

In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features.

Representation Learning

Boundary-Seeking Generative Adversarial Networks

6 code implementations27 Feb 2017 R. Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio

We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.

Scene Understanding Text Generation

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

no code implementations26 Feb 2017 Tong Che, Yan-ran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio

Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted.

Mode Regularized Generative Adversarial Networks

no code implementations7 Dec 2016 Tong Che, Yan-ran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes.

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