b'Zhangjie Cao'

research

∙ 11/13/2022

Out-of-Dynamics Imitation Learning from Multimodal Demonstrations

Existing imitation learning works mainly assume that the demonstrator wh...

0 Yiwen Qiu, et al. ∙

research

∙ 09/16/2022

Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Multimodal demonstrations provide robots with an abundance of informatio...

6 Yilun Hao, et al. ∙

research

∙ 06/08/2022

Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models

Transfer learning aims to leverage knowledge from pre-trained models to ...

22 Yang Shu, et al. ∙

research

∙ 04/15/2022

MetaSets: Meta-Learning on Point Sets for Generalizable Representations

Deep learning techniques for point clouds have achieved strong performan...

5 Chao Huang, et al. ∙

research

∙ 03/14/2022

From Big to Small: Adaptive Learning to Partial-Set Domains

Domain adaptation targets at knowledge acquisition and dissemination fro...

14 Zhangjie Cao, et al. ∙

research

∙ 03/08/2022

Leveraging Smooth Attention Prior for Multi-Agent Trajectory Prediction

Multi-agent interactions are important to model for forecasting other ag...

5 Zhangjie Cao, et al. ∙

research

∙ 03/02/2022

Weakly Supervised Correspondence Learning

Correspondence learning is a fundamental problem in robotics, which aims...

20 Zihan Wang, et al. ∙

research

∙ 02/07/2022

Learning from Imperfect Demonstrations via Adversarial Confidence Transfer

Existing learning from demonstration algorithms usually assume access to...

2 Zhangjie Cao, et al. ∙

research

∙ 10/28/2021

Learning Feasibility to Imitate Demonstrators with Different Dynamics

The goal of learning from demonstrations is to learn a policy for an age...

0 Zhangjie Cao, et al. ∙

research

∙ 10/27/2021

Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality

Most existing imitation learning approaches assume the demonstrations ar...

0 Songyuan Zhang, et al. ∙

research

∙ 10/14/2021

Omni-Training for Data-Efficient Deep Learning

Learning a generalizable deep model from a few examples in a short time ...

0 Yang Shu, et al. ∙

research

∙ 07/10/2021

Multi-Agent Imitation Learning with Copulas

Multi-agent imitation learning aims to train multiple agents to perform ...

7 Hongwei Wang, et al. ∙

research

∙ 06/29/2021

Zoo-Tuning: Adaptive Transfer from a Zoo of Models

With the development of deep networks on various large-scale datasets, a...

0 Yang Shu, et al. ∙

research

∙ 04/08/2021

Open Domain Generalization with Domain-Augmented Meta-Learning

Leveraging datasets available to learn a model with high generalization ...

0 Yang Shu, et al. ∙

research

∙ 03/10/2021

Learning from Imperfect Demonstrations from Agents with Varying Dynamics

Imitation learning enables robots to learn from demonstrations. Previous...

0 Zhangjie Cao, et al. ∙

research

∙ 02/10/2021

Transfer Reinforcement Learning across Homotopy Classes

The ability for robots to transfer their learned knowledge to new tasks ...

0 Zhangjie Cao, et al. ∙

research

∙ 07/01/2020

Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving

Autonomous driving has achieved significant progress in recent years, bu...

7 Zhangjie Cao, et al. ∙

research

∙ 06/07/2020

Robust Learning Through Cross-Task Consistency

Visual perception entails solving a wide set of tasks, e.g., object dete...

12 Amir Zamir, et al. ∙

research

∙ 11/21/2019

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

Domain adaptation aims to leverage the supervision signal of source doma...

0 Yuxuan Song, et al. ∙

research

∙ 06/27/2019

Few-Shot Video Classification via Temporal Alignment

There is a growing interest in learning a model which could recognize no...

5 Kaidi Cao, et al. ∙

research

∙ 05/30/2019

AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

Given unpaired data from multiple domains, a key challenge is to efficie...

6 Aditya Grover, et al. ∙

research

∙ 03/28/2019

Learning to Transfer Examples for Partial Domain Adaptation

Domain adaptation is critical for learning in new and unseen environment...

12 Zhangjie Cao, et al. ∙

research

∙ 09/04/2018

Multi-Adversarial Domain Adaptation

Recent advances in deep domain adaptation reveal that adversarial learni...

0 Zhongyi Pei, et al. ∙

research

∙ 09/04/2018

Deep Priority Hashing

Deep hashing enables image retrieval by end-to-end learning of deep repr...

8 Zhangjie Cao, et al. ∙

research

∙ 08/10/2018

Partial Adversarial Domain Adaptation

Domain adversarial learning aligns the feature distributions across the ...

0 Zhangjie Cao, et al. ∙

research

∙ 12/13/2017

Transfer Adversarial Hashing for Hamming Space Retrieval

Hashing is widely applied to large-scale image retrieval due to the stor...

0 Zhangjie Cao, et al. ∙

research

∙ 12/12/2017

3D Object Classification via Spherical Projections

In this paper, we introduce a new method for classifying 3D objects. Our...

0 Zhangjie Cao, et al. ∙

research

∙ 02/02/2017

HashNet: Deep Learning to Hash by Continuation

Learning to hash has been widely applied to approximate nearest neighbor...

0 Zhangjie Cao, et al. ∙

research

∙ 08/15/2016

Transitive Hashing Network for Heterogeneous Multimedia Retrieval

Hashing has been widely applied to large-scale multimedia retrieval due ...

0 Zhangjie Cao, et al. ∙

Zhangjie Cao

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