1 code implementation • 18 May 2025 • Hanyu Wang, Xinrui Wu, Zijian Ding, Su Zheng, Chengyue Wang, Tony Nowatzki, Yizhou Sun, Jason Cong
Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle.
1 code implementation • 12 Mar 2025 • Jihao Zhao, Zhiyuan Ji, Zhaoxin Fan, Hanyu Wang, Simin Niu, Bo Tang, Feiyu Xiong, Zhiyu Li
Retrieval-Augmented Generation (RAG), while serving as a viable complement to large language models (LLMs), often overlooks the crucial aspect of text chunking within its pipeline.
1 code implementation • 10 Mar 2025 • Xun Liang, Hanyu Wang, Huayi Lai, Simin Niu, Shichao Song, Jiawei Yang, Jihao Zhao, Feiyu Xiong, Bo Tang, Zhiyu Li
Notably, at 50% pruning, SEAP surpasses both WandA and FLAP by over 20%, and at 20% pruning, it incurs only a 2. 2% performance drop compared to the dense model.
no code implementations • 3 Mar 2025 • Hanyu Wang, Emmanuel K. Tsinda, Anthony J. Dunn, Francis Chikweto, Alain B. Zemkoho
For organisms with large, similar genomes like Escherichia coli and Shigella flexneri, differentiating between species is also difficult but crucial.
1 code implementation • 20 Feb 2025 • Xun Liang, Jiawei Yang, Yezhaohui Wang, Chen Tang, Zifan Zheng, Simin Niu, Shichao Song, Hanyu Wang, Bo Tang, Feiyu Xiong, Keming Mao, Zhiyu Li
Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation.
no code implementations • 6 Feb 2025 • Hanyu Wang, Bochuan Cao, Yuanpu Cao, Jinghui Chen
Large language models (LLMs) are known to struggle with consistently generating truthful responses.
1 code implementation • 28 Jan 2025 • Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Hanyu Wang, Feiyu Xiong, Jason Zhaoxin Fan, Bo Tang, Shichao Song, Mengwei Wang, Jiawei Yang
However, the incorporation of external and unverified knowledge increases the vulnerability of LLMs because attackers can perform attack tasks by manipulating knowledge.
no code implementations • 21 Jan 2025 • Jilan Cheng, Guoli Long, Zeyu Zhang, Zhenjia Qi, Hanyu Wang, Libin Lu, Shuihua Wang, Yudong Zhang, Jin Hong
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment.
1 code implementation • 28 Oct 2024 • Hanyu Wang, Saksham Suri, Yixuan Ren, Hao Chen, Abhinav Shrivastava
By incorporating the prior model during training, LARP learns a latent space that is not only optimized for video reconstruction but is also structured in a way that is more conducive to autoregressive generation.
Ranked #3 on
Video Generation
on UCF-101
no code implementations • 22 Oct 2024 • Leyao Wang, Yu Wang, Bo Ni, Yuying Zhao, Hanyu Wang, Yao Ma, Tyler Derr
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes.
1 code implementation • 7 Oct 2024 • Qingchen Yu, Shichao Song, Ke Fang, Yunfeng Shi, Zifan Zheng, Hanyu Wang, Simin Niu, Zhiyu Li
This approach allows for the relatively dynamic generation of evaluation datasets, mitigating the risk of model cheating while aligning assessments more closely with genuine user needs for reasoning capabilities, thus enhancing the reliability of evaluations.
1 code implementation • 22 Aug 2024 • Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, Zhiyu Li
This paper systematically reviews the latest advancements in CTG for LLMs, offering a comprehensive definition of its core concepts and clarifying the requirements for control conditions and text quality.
1 code implementation • 19 Jul 2024 • Xun Liang, Shichao Song, Zifan Zheng, Hanyu Wang, Qingchen Yu, Xunkai Li, Rong-Hua Li, Yi Wang, Zhonghao Wang, Feiyu Xiong, Zhiyu Li
In this paper, we use a unified perspective of internal consistency, offering explanations for reasoning deficiencies and hallucinations.
1 code implementation • 30 Jun 2024 • Yanfang Chen, Ding Chen, Shichao Song, Simin Niu, Hanyu Wang, Zeyun Tang, Feiyu Xiong, Zhiyu Li
HealthRCN is the largest known dataset of Chinese health information rumors to date.
1 code implementation • 27 May 2024 • Xun Liang, Simin Niu, Zhiyu Li, Sensen Zhang, Shichao Song, Hanyu Wang, Jiawei Yang, Feiyu Xiong, Bo Tang, Chenyang Xi
Retrieval-Augmented Generation (RAG) offers a cost-effective approach to injecting real-time knowledge into large language models (LLMs).
no code implementations • 15 Mar 2024 • Haoyue Tang, Tian Xie, Aosong Feng, Hanyu Wang, Chenyang Zhang, Yang Bai
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
1 code implementation • 17 Feb 2024 • Xun Liang, Hanyu Wang, Shichao Song, Mengting Hu, Xunzhi Wang, Zhiyu Li, Feiyu Xiong, Bo Tang
In this study, we introduce a pluggable CTG framework for Large Language Models (LLMs) named Dynamic Attribute Graphs-based controlled text generation (DATG).
1 code implementation • 4 Dec 2023 • Hanyu Wang, Pengxiang Wu, Kevin Dela Rosa, Chen Wang, Abhinav Shrivastava
Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images.
no code implementations • 20 Oct 2023 • Wenhao Yu, Jie Peng, Quecheng Qiu, Hanyu Wang, Lu Zhang, Jianmin Ji
However, two roadblocks arise for training a DRL policy that outputs paths: (1) The action space for potential paths often involves higher dimensions comparing to low-level commands, which increases the difficulties of training; (2) It takes multiple time steps to track a path instead of a single time step, which requires the path to predicate the interactions of the robot w. r. t.
no code implementations • ICCV 2023 • Nirat Saini, Hanyu Wang, Archana Swaminathan, Vinoj Jayasundara, Bo He, Kamal Gupta, Abhinav Shrivastava
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
no code implementations • CVPR 2023 • Bo He, Xitong Yang, Hanyu Wang, Zuxuan Wu, Hao Chen, Shuaiyi Huang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
Implicit neural representations (INR) have gained increasing attention in representing 3D scenes and images, and have been recently applied to encode videos (e. g., NeRV, E-NeRV).
1 code implementation • CVPR 2023 • Shishira R Maiya, Sharath Girish, Max Ehrlich, Hanyu Wang, Kwot Sin Lee, Patrick Poirson, Pengxiang Wu, Chen Wang, Abhinav Shrivastava
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
no code implementations • 25 Sep 2022 • Hanyu Wang, Emmanuel K. Tsinda, Anthony J. Dunn, Francis Chikweto, Nusreen Ahmed, Emanuela Pelosi, Alain B. Zemkoho
Hence, in this paper, we develop a semi-automated method to design both forward and reverse primer sets to detect SARS-CoV-2 variants.
no code implementations • 18 Apr 2022 • Hanyu Wang, Kamal Gupta, Larry Davis, Abhinav Shrivastava
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
3 code implementations • NeurIPS 2021 • Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser-Nam Lim, Abhinav Shrivastava
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Ranked #7 on
Video Reconstruction
on UVG
1 code implementation • 4 Dec 2020 • Shubham Rai, Walter Lau Neto, Yukio Miyasaka, Xinpei Zhang, Mingfei Yu, Qingyang Yi Masahiro Fujita, Guilherme B. Manske, Matheus F. Pontes, Leomar S. da Rosa Junior, Marilton S. de Aguiar, Paulo F. Butzen, Po-Chun Chien, Yu-Shan Huang, Hoa-Ren Wang, Jie-Hong R. Jiang, Jiaqi Gu, Zheng Zhao, Zixuan Jiang, David Z. Pan, Brunno A. de Abreu, Isac de Souza Campos, Augusto Berndt, Cristina Meinhardt, Jonata T. Carvalho, Mateus Grellert, Sergio Bampi, Aditya Lohana, Akash Kumar, Wei Zeng, Azadeh Davoodi, Rasit O. Topaloglu, Yuan Zhou, Jordan Dotzel, Yichi Zhang, Hanyu Wang, Zhiru Zhang, Valerio Tenace, Pierre-Emmanuel Gaillardon, Alan Mishchenko, Satrajit Chatterjee
If the function is incompletely-specified, the implementation has to be true only on the care set.
no code implementations • ECCV 2018 • Hanyu Wang, Jianwei Guo, Dong-Ming Yan, Weize Quan, Xiaopeng Zhang
In this paper, we present a novel deep learning framework that derives discriminative local descriptors for 3D surface shapes.