Search Results for author: He Zhu

Found 27 papers, 9 papers with code

Prompt-based Personalized Federated Learning for Medical Visual Question Answering

no code implementations15 Feb 2024 He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama

We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns in traditional medical visual question answering (VQA) methods.

Medical Visual Question Answering Personalized Federated Learning +2

Formal-LLM: Integrating Formal Language and Natural Language for Controllable LLM-based Agents

1 code implementation1 Feb 2024 Zelong Li, Wenyue Hua, Hao Wang, He Zhu, Yongfeng Zhang

A stack-based LLM plan generation process is then conducted under the supervision of the automaton to ensure that the generated plan satisfies the constraints, making the planning process controllable.

SAM-guided Graph Cut for 3D Instance Segmentation

no code implementations13 Dec 2023 Haoyu Guo, He Zhu, Sida Peng, Yuang Wang, Yujun Shen, Ruizhen Hu, Xiaowei Zhou

Experimental results on the ScanNet, ScanNet++ and KITTI-360 datasets demonstrate that our method achieves robust segmentation performance and can generalize across different types of scenes.

3D Instance Segmentation Segmentation +1

Attention Schema in Neural Agents

no code implementations27 May 2023 Dianbo Liu, Samuele Bolotta, He Zhu, Yoshua Bengio, Guillaume Dumas

A strong prediction of this theory is that an agent can use its own AS to also infer the states of other agents' attention and consequently enhance coordination with other agents.

Descriptive Multi-agent Reinforcement Learning

HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

1 code implementation24 May 2023 He Zhu, Chong Zhang, JunJie Huang, Junran Wu, Ke Xu

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure.

Multi-Label Classification text-classification +1

Adaptive Data Augmentation for Contrastive Learning

no code implementations5 Apr 2023 Yuhan Zhang, He Zhu, Shan Yu

In computer vision, contrastive learning is the most advanced unsupervised learning framework.

Contrastive Learning Data Augmentation

TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions

no code implementations9 Mar 2023 He Zhu, Xihua Li, Xuemin Zhao, Yunbo Cao, Shan Yu

Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively.

Contrastive Learning Representation Learning +1

Interpretable Visual Question Answering Referring to Outside Knowledge

no code implementations8 Mar 2023 He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama

We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations.

Image Captioning Question Answering +1

SoccerNet 2022 Challenges Results

7 code implementations5 Oct 2022 Silvio Giancola, Anthony Cioppa, Adrien Deliège, Floriane Magera, Vladimir Somers, Le Kang, Xin Zhou, Olivier Barnich, Christophe De Vleeschouwer, Alexandre Alahi, Bernard Ghanem, Marc Van Droogenbroeck, Abdulrahman Darwish, Adrien Maglo, Albert Clapés, Andreas Luyts, Andrei Boiarov, Artur Xarles, Astrid Orcesi, Avijit Shah, Baoyu Fan, Bharath Comandur, Chen Chen, Chen Zhang, Chen Zhao, Chengzhi Lin, Cheuk-Yiu Chan, Chun Chuen Hui, Dengjie Li, Fan Yang, Fan Liang, Fang Da, Feng Yan, Fufu Yu, Guanshuo Wang, H. Anthony Chan, He Zhu, Hongwei Kan, Jiaming Chu, Jianming Hu, Jianyang Gu, Jin Chen, João V. B. Soares, Jonas Theiner, Jorge De Corte, José Henrique Brito, Jun Zhang, Junjie Li, Junwei Liang, Leqi Shen, Lin Ma, Lingchi Chen, Miguel Santos Marques, Mike Azatov, Nikita Kasatkin, Ning Wang, Qiong Jia, Quoc Cuong Pham, Ralph Ewerth, Ran Song, RenGang Li, Rikke Gade, Ruben Debien, Runze Zhang, Sangrok Lee, Sergio Escalera, Shan Jiang, Shigeyuki Odashima, Shimin Chen, Shoichi Masui, Shouhong Ding, Sin-wai Chan, Siyu Chen, Tallal El-Shabrawy, Tao He, Thomas B. Moeslund, Wan-Chi Siu, Wei zhang, Wei Li, Xiangwei Wang, Xiao Tan, Xiaochuan Li, Xiaolin Wei, Xiaoqing Ye, Xing Liu, Xinying Wang, Yandong Guo, YaQian Zhao, Yi Yu, YingYing Li, Yue He, Yujie Zhong, Zhenhua Guo, Zhiheng Li

The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.

Action Spotting Camera Calibration +3

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)

1 code implementation23 Aug 2022 Hanxiong Chen, Yunqi Li, He Zhu, Yongfeng Zhang

Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures.

Neural Architecture Search

Defending Observation Attacks in Deep Reinforcement Learning via Detection and Denoising

1 code implementation14 Jun 2022 Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan

We focus our attention on well-trained deterministic and stochastic neural network policies in the context of continuous control benchmarks subject to four well-studied observation space adversarial attacks.

Continuous Control Denoising +2

Graph Collaborative Reasoning

no code implementations27 Dec 2021 Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu, Yongfeng Zhang

Graphs can represent relational information among entities and graph structures are widely used in many intelligent tasks such as search, recommendation, and question answering.

Link Prediction Logical Reasoning +2

Differentiable Synthesis of Program Architectures

no code implementations NeurIPS 2021 Guofeng Cui, He Zhu

Differentiable programs have recently attracted much interest due to their interpretability, compositionality, and their efficiency to leverage differentiable training.

Program Synthesis

ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models

1 code implementation22 Oct 2021 Ziheng Chen, Fabrizio Silvestri, Jia Wang, He Zhu, Hongshik Ahn, Gabriele Tolomei

However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.

counterfactual Decision Making +2

Hierarchical information matters: Text classification via tree based graph neural network

2 code implementations COLING 2022 Chong Zhang, He Zhu, Xingyu Peng, Junran Wu, Ke Xu

Inspired by the structural entropy, we construct the coding tree of the graph by minimizing the structural entropy and propose HINT, which aims to make full use of the hierarchical information contained in the text for the task of text classification.

Dependency Parsing text-classification +1

Programmatic Reinforcement Learning without Oracles

no code implementations ICLR 2022 Wenjie Qiu, He Zhu

Our first contribution is a programmatically interpretable RL framework that conducts program architecture search on top of a continuous relaxation of the architecture space defined by programming language grammar rules.

Bilevel Optimization Policy Gradient Methods +2

Intra-Class Uncertainty Loss Function for Classification

no code implementations12 Apr 2021 He Zhu, Shan Yu

To address this issue, we propose a loss function with intra-class uncertainty following Gaussian distribution.

Classification General Classification

Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation

no code implementations31 Dec 2020 Yichi Zhang, Qingcheng Liao, Lin Yuan, He Zhu, Jiezhen Xing, Jicong Zhang

In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation.

Computed Tomography (CT) Lesion Segmentation +1

Patient similarity: methods and applications

no code implementations1 Dec 2020 Leyu Dai, He Zhu, Dianbo Liu

Patient similarity analysis is important in health care applications.

Data Integration

FakeSafe: Human Level Data Protection by Disinformation Mapping using Cycle-consistent Adversarial Network

no code implementations23 Nov 2020 He Zhu, Dianbo Liu

The concept of disinformation is to use fake messages to confuse people in order to protect the real information.

Generative Adversarial Network

Robustness to Adversarial Attacks in Learning-Enabled Controllers

no code implementations11 Jun 2020 Zikang Xiong, Joe Eappen, He Zhu, Suresh Jagannathan

We consider shield-based defenses as a means to improve controller robustness in the face of such perturbations.

Continuous Control

Federated machine learning with Anonymous Random Hybridization (FeARH) on medical records

no code implementations25 Dec 2019 Jianfei Cui, He Zhu, Hao Deng, Ziwei Chen, Dianbo Liu

Sometimes electrical medical records are restricted and difficult to centralize for machine learning, which could only be trained in distributed manner that involved many institutions in the process.

BIG-bench Machine Learning

ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks

1 code implementation17 Jul 2019 Xuankang Lin, He Zhu, Roopsha Samanta, Suresh Jagannathan

Our key insight is that we can integrate an optimization-based abstraction refinement loop into the learning process and operate over dynamically constructed partitions of the input space that considers accuracy and safety objectives synergistically.

BIG-bench Machine Learning Collision Avoidance

An Inductive Synthesis Framework for Verifiable Reinforcement Learning

no code implementations16 Jul 2019 He Zhu, Zikang Xiong, Stephen Magill, Suresh Jagannathan

Rather than enforcing safety by examining and altering the structure of a complex neural network implementation, our technique uses blackbox methods to synthesizes deterministic programs, simpler, more interpretable, approximations of the network that can nonetheless guarantee desired safety properties are preserved, even when the network is deployed in unanticipated or previously unobserved environments.

BIG-bench Machine Learning reinforcement-learning +1

Distributed Data Vending on Blockchain

no code implementations15 Mar 2018 Jiayu Zhou, Fengyi Tang, He Zhu, Ning Nan, Ziheng Zhou

However, one key challenge in distributed data vending is the trade-off dilemma between the effectiveness of data retrieval, and the leakage risk from indexing the data.

Retrieval

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