500 citations · 2k across the 65 of their papers we have counts for
13 papers · 1 filter
Out-of-Dynamics Imitation Learning from Multimodal Demonstrations
Yiwen Qiu, Jialong Wu, Zhangjie Cao +1
Existing imitation learning works mainly assume that the demonstrator who collects demonstrations shares the same dynamics as the imitator. However, the assumption limits the usage…
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
Haixu Wu, Tengge Hu, Yong Liu +3
Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal vari…
Recommender Transformers with Behavior Pathways
Zhiyu Yao, Xinyang Chen, Sinan Wang +4
Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behav…
Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models
Yang Shu, Zhangjie Cao, Ziyang Zhang +2
Transfer learning aims to leverage knowledge from pre-trained models to benefit the target task. Prior transfer learning work mainly transfers from a single model. However, with th…
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
Yong Liu, Haixu Wu, Jianmin Wang +1
Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real…
A Roadmap for Big Model
Sha Yuan, Hanyu Zhao, Shuai Zhao +97
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the c…
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
Chao Huang, Zhangjie Cao, Yunbo Wang +2
Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critic…
Continual Predictive Learning from Videos
Geng Chen, Wendong Zhang, Han Lu +4
Predictive learning ideally builds the world model of physical processes in one or more given environments. Typical setups assume that we can collect data from all environments at…
From Big to Small: Adaptive Learning to Partial-Set Domains
Zhangjie Cao, Kaichao You, Ziyang Zhang +2
Domain adaptation targets at knowledge acquisition and dissemination from a labeled source domain to an unlabeled target domain under distribution shift. Still, the common requirem…
Debiased Self-Training for Semi-Supervised Learning
Baixu Chen, Junguang Jiang, Ximei Wang +3
Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor-exhausti…
Flowformer: Linearizing Transformers with Conservation Flows
Haixu Wu, Jialong Wu, Jiehui Xu +2
Transformers based on the attention mechanism have achieved impressive success in various areas. However, the attention mechanism has a quadratic complexity, significantly impeding…
Supported Policy Optimization for Offline Reinforcement Learning
Jialong Wu, Haixu Wu, Zihan Qiu +2
Policy constraint methods to offline reinforcement learning (RL) typically utilize parameterization or regularization that constrains the policy to perform actions within the suppo…
Transferability in Deep Learning: A Survey
Junguang Jiang, Yang Shu, Jianmin Wang +1
The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relev…