no code implementations • 9 Oct 2024 • Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
We introduce DA-Code, a code generation benchmark specifically designed to assess LLMs on agent-based data science tasks.
no code implementations • 16 Aug 2024 • Jun Zhou, Chunsheng Liu, Faliang Chang, Wenqian Wang, Penghui Hao, Yiming Huang, Zhiqiang Yang
Associating driver attention with driving scene across two fields of views (FOVs) is a hard cross-domain perception problem, which requires comprehensive consideration of cross-view mapping, dynamic driving scene analysis, and driver status tracking.
no code implementations • 29 Jul 2024 • Yiming Huang, Beilei Cui, Ikemura Kei, Jiekai Zhang, Long Bai, Hongliang Ren
In this paper, we propose a novel strategy for dynamic surgical neural scene registration.
no code implementations • CVPR 2024 • Kei Ikemura, Yiming Huang, Felix Heide, Zhaoxiang Zhang, Qifeng Chen, Chenyang Lei
Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios, such as in the presence of transparent or reflective objects.
no code implementations • 20 Mar 2024 • Yiming Huang, Weilin Wan, Yue Yang, Chris Callison-Burch, Mark Yatskar, Lingjie Liu
Text-to-motion models excel at efficient human motion generation, but existing approaches lack fine-grained controllability over the generation process.
no code implementations • 4 Mar 2024 • Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, Weizhu Chen
Large language models (LLMs) have shown great potential in complex reasoning tasks, yet their performance is often hampered by the scarcity of high-quality and reasoning-focused training datasets.
Ranked #51 on Math Word Problem Solving on MATH
1 code implementation • 21 Feb 2024 • Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu
Large Language Models (LLMs) have revolutionized open-domain dialogue agents but encounter challenges in multi-character role-playing (MCRP) scenarios.
2 code implementations • 29 Jan 2024 • Yiming Huang, Beilei Cui, Long Bai, Ziqi Guo, Mengya Xu, Mobarakol Islam, Hongliang Ren
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes.
no code implementations • 7 Dec 2023 • Weilin Wan, Yiming Huang, Shutong Wu, Taku Komura, Wenping Wang, Dinesh Jayaraman, Lingjie Liu
In this study, we introduce a learning-based method for generating high-quality human motion sequences from text descriptions (e. g., ``A person walks forward").
no code implementations • 4 Dec 2023 • Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem recently.
2 code implementations • CVPR 2024 • Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei HUANG, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, David Crandall, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C. V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
1 code implementation • 3 Nov 2023 • Qiang Wu, Yiming Huang, Yujie Zeng, Yijie Teng, Fang Zhou, Linyuan Lü
Here, we introduce a Cooperative Network Learning (CNL) framework to ensure secure graph computing for various graph tasks.
no code implementations • 23 Oct 2023 • Fangyu Lei, Tongxu Luo, Pengqi Yang, Weihao Liu, Hanwen Liu, Jiahe Lei, Yiming Huang, Yifan Wei, Shizhu He, Jun Zhao, Kang Liu
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.
2 code implementations • 23 Oct 2023 • Fangyu Lei, Qian Liu, Yiming Huang, Shizhu He, Jun Zhao, Kang Liu
The rapid development of Large Language Models (LLMs) has led to great strides in model capabilities like long-context understanding and reasoning.
1 code implementation • 22 Sep 2023 • Yiming Huang, Yujie Zeng, Qiang Wu, Linyuan Lü
Despite the recent successes of vanilla Graph Neural Networks (GNNs) on various tasks, their foundation on pairwise networks inherently limits their capacity to discern latent higher-order interactions in complex systems.
Ranked #1 on Node Classification on Wisconsin
no code implementations • 19 Sep 2023 • Yiming Huang, Huiyuan Wang, Yuxuan Du, Xiao Yuan
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges.
1 code implementation • 29 Aug 2023 • Yiming Huang, Guole Liu, Yaoru Luo, Ge Yang
We then apply differentiable top-k feature adaptation to train the patch descriptor, mapping the extracted feature representations to a new vector space, enabling effective detection of anomalies.
no code implementations • 23 Aug 2023 • ZiHao Wang, Yiming Huang, Ziyu Zhou
We introduce this model with the novel structure as the Context Clustering Generative Adversarial Network (CoC-GAN), which offers a distinctive viewpoint in the domain of feature aggregating and dispatching.
no code implementations • 20 Aug 2023 • Yiming Huang, Aozhe Jia, Xiaodan Zhang, Jiawei Zhang
In this paper, we propose a weighted relevancy strategy, which takes the importance of token values into consideration, to reduce distortion when equally accumulating relevance.
no code implementations • 11 Jul 2023 • Yujie Zeng, Yiming Huang, Qiang Wu, Linyuan Lü
It can tackle higher-order tasks by leveraging novel higher-order presentations: hierarchical bipartite graphs and higher-order hierarchical (HoH) Laplacians, where targeted simplices are grouped into a hub set and can interact with other simplices.
1 code implementation • 19 May 2023 • Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
no code implementations • 29 May 2022 • James J Gugger, Nishant Sinha, Yiming Huang, Alexa Walter, Cillian Lynch, Justin Morrison, Nathan Smyk, Danielle Sandsmark, Ramon Diaz-Arrastia, Kathryn A Davis
We hypothesized that changes in structural brain network abnormalities guide the trajectory of an individual's recovery post-injury.
1 code implementation • NeurIPS 2019 • Shouvanik Chakrabarti, Yiming Huang, Tongyang Li, Soheil Feizi, Xiaodi Wu
The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines.
no code implementations • 2 Nov 2016 • Yiming Huang, Xiaoyu Li
While classical Laplacian eigenmap algorithm requires polynomial time to solve the eigenvector problem, our algorithm is able to exponentially speed up nonlinear dimensionality reduction.