Search Results for author: WenHao Zhang

Found 22 papers, 10 papers with code

Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for Foundation Models

2 code implementations23 Dec 2024 Daoyuan Chen, Yilun Huang, Xuchen Pan, Nana Jiang, Haibin Wang, Ce Ge, Yushuo Chen, WenHao Zhang, Zhijian Ma, Yilei Zhang, Jun Huang, Wei Lin, Yaliang Li, Bolin Ding, Jingren Zhou

The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast valuable data with varied types utilized by these models.

Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature

1 code implementation13 Nov 2024 WenHao Zhang, Matias Quintana, Clayton Miller

The indoor environment significantly impacts human health and well-being; enhancing health and reducing energy consumption in these settings is a central research focus.

Collaborative Filtering Product Recommendation +4

The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective

1 code implementation11 Jul 2024 Zhen Qin, Daoyuan Chen, WenHao Zhang, Liuyi Yao, Yilun Huang, Bolin Ding, Yaliang Li, Shuiguang Deng

As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition.

SCSA: Exploring the Synergistic Effects Between Spatial and Channel Attention

1 code implementation6 Jul 2024 Yunzhong Si, Huiying Xu, Xinzhong Zhu, WenHao Zhang, Yao Dong, Yuxing Chen, Hongbo Li

Our SCSA consists of two parts: the Shareable Multi-Semantic Spatial Attention (SMSA) and the Progressive Channel-wise Self-Attention (PCSA).

Classification object-detection +2

Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction

no code implementations27 May 2024 WenHao Zhang, Bin Huang, Shuyue Chen, Xiaoling Xu, Weiwen Wu, Qiegen Liu

During the prior learning stage, the projection data is first transformed into multiple partitioned Hankel matrices.

CT Reconstruction

ExcluIR: Exclusionary Neural Information Retrieval

1 code implementation26 Apr 2024 WenHao Zhang, Mengqi Zhang, Shiguang Wu, Jiahuan Pei, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Pengjie Ren

However, in information retrieval community, there is little research on exclusionary retrieval, where users express what they do not want in their queries.

Information Retrieval Retrieval

MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading

1 code implementation18 Apr 2024 WenHao Zhang, Jun Wang, Yong Luo, Lei Yu, Wei Yu, Zheng He, Jialie Shen

Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated.

Lip Reading

Aligning Individual and Collective Objectives in Multi-Agent Cooperation

no code implementations19 Feb 2024 Yang Li, WenHao Zhang, Jianhong Wang, Shao Zhang, Yali Du, Ying Wen, Wei Pan

Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals.

SMAC+ Starcraft +1

Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification

no code implementations12 Dec 2023 Yuwei Guo, WenHao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, Fang Liu

Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities.

Data Augmentation Person Re-Identification

ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination

2 code implementations8 Oct 2023 Xihuai Wang, Shao Zhang, WenHao Zhang, Wentao Dong, Jingxiao Chen, Ying Wen, Weinan Zhang

ZSC-Eval consists of: 1) Generation of evaluation partner candidates through behavior-preferring rewards to approximate deployment-time partners' distribution; 2) Selection of evaluation partners by Best-Response Diversity (BR-Div); 3) Measurement of generalization performance with various evaluation partners via the Best-Response Proximity (BR-Prox) metric.

Diversity Multi-agent Reinforcement Learning

Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination

1 code implementation5 Jun 2023 Yang Li, Shao Zhang, Jichen Sun, WenHao Zhang, Yali Du, Ying Wen, Xinbing Wang, Wei Pan

In order to solve cooperative incompatibility in learning and effectively address the problem in the context of ZSC, we introduce the Cooperative Open-ended LEarning (COLE) framework, which formulates open-ended objectives in cooperative games with two players using perspectives of graph theory to evaluate and pinpoint the cooperative capacity of each strategy.

AI Agent

Causal Inference in medicine and in health policy, a summary

no code implementations10 May 2021 WenHao Zhang, Ramin Ramezani, Arash Naeim

We will discuss causal inference and ways to discover the cause-effect from observational studies in healthcare domain.

BIG-bench Machine Learning Causal Inference

Domestic activities clustering from audio recordings using convolutional capsule autoencoder network

no code implementations8 May 2021 Ziheng Lin, Yanxiong Li, Zhangjin Huang, WenHao Zhang, Yufeng Tan, YiChun Chen, Qianhua He

Domestic activities clustering from audio recordings aims at merging audio clips which belong to the same class of domestic activity into a single cluster.

Clustering

A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits

1 code implementation NeurIPS 2019 Wenhao Zhang, Si Wu, Brent Doiron, Tai Sing Lee

This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits.

Causal Inference Model Selection

WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning

no code implementations17 Oct 2019 Wenhao Zhang, Ramin Ramezani, Arash Naeim

In this paper, we propose a novel method that combines a Weighted Oversampling Technique and ensemble Boosting method (WOTBoost) to improve the classification accuracy of minority data without sacrificing the accuracy of the majority class.

Fraud Detection General Classification +1

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