Search Results for author: Wenjing Yang

Found 14 papers, 4 papers with code

Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory

no code implementations2 Dec 2022 Zhen Liang, Changyuan Zhao, Wanwei Liu, Bai Xue, Wenjing Yang, Zhengbin Pang

Based on Koopman operator theory, this paper presents an alternative perspective of linear dynamics on dealing with the credit assignment problem for trained neural networks.

GANet: Goal Area Network for Motion Forecasting

no code implementations20 Sep 2022 Mingkun Wang, Xinge Zhu, Changqian Yu, Wei Li, Yuexin Ma, Ruochun Jin, Xiaoguang Ren, Dongchun Ren, Mingxu Wang, Wenjing Yang

In view of this, we propose a new goal area-based framework, named Goal Area Network (GANet), for motion forecasting, which models goal areas rather than exact goal coordinates as preconditions for trajectory prediction, performing more robustly and accurately.

Motion Forecasting Trajectory Prediction

Recent Advances for Quantum Neural Networks in Generative Learning

no code implementations7 Jun 2022 Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, DaCheng Tao

Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.

BIG-bench Machine Learning Quantum Machine Learning

Graph Adversarial Self-Supervised Learning

no code implementations NeurIPS 2021 Longqi Yang, Liangliang Zhang, Wenjing Yang

This paper studies a long-standing problem of learning the representations of a whole graph without human supervision.

Graph Classification Self-Supervised Learning

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation

1 code implementation NeurIPS 2021 Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok

To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.

Domain Adaptation

Conditional Variational Capsule Network for Open Set Recognition

1 code implementation ICCV 2021 Yunrui Guo, Guglielmo Camporese, Wenjing Yang, Alessandro Sperduti, Lamberto Ballan

In this way, we are able to control the compactness of the features of the same class around the center of the gaussians, thus controlling the ability of the classifier in detecting samples from unknown classes.

Open Set Learning

Meta Discovery: Learning to Discover Novel Classes given Very Limited Data

1 code implementation ICLR 2022 Haoang Chi, Feng Liu, Bo Han, Wenjing Yang, Long Lan, Tongliang Liu, Gang Niu, Mingyuan Zhou, Masashi Sugiyama

In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes.

Meta-Learning Novel Class Discovery

Network Clustering for Multi-task Learning

no code implementations22 Jan 2021 Dehong Gao, Wenjing Yang, Huiling Zhou, Yi Wei, Yi Hu, Hao Wang

The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task.

Document Classification Multi-Task Learning

Generating an Overview Report over Many Documents

no code implementations17 Aug 2019 Jingwen Wang, Hao Zhang, Cheng Zhang, Wenjing Yang, Liqun Shao, Jie Wang

To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.

Decision Making Document Summarization +1

An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview

no code implementations25 Nov 2018 Bo Zhang, Bin Chen, Jinyu Yang, Wenjing Yang, Jiankang Zhang

Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system.

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