1 code implementation • 7 Jul 2025 • Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang
In this work, we propose VOTE, an efficient and general framework for the optimization and acceleration of VLA models.
no code implementations • 23 Jun 2025 • Lu Wang, Di Zhang, Fangkai Yang, Pu Zhao, Jianfeng Liu, Yuefeng Zhan, Hao Sun, QIngwei Lin, Weiwei Deng, Dongmei Zhang, Feng Sun, Qi Zhang
This work enhances profile generation as a key innovation for next-generation recommendation systems.
no code implementations • 30 May 2025 • Qihui Fan, Enfu Nan, Wenbo Li, Lei Lu, Pu Zhao, Yanzhi Wang
The growing popularity of social deduction game systems for both business applications and AI research has greatly benefited from the rapid advancements in Large Language Models (LLMs), which now demonstrate stronger reasoning and persuasion capabilities.
1 code implementation • 28 May 2025 • Zhenglun Kong, Zheng Zhan, Shiyue Hou, Yifan Gong, Xin Meng, Pengwei Sui, Peiyan Dong, Xuan Shen, Zifeng Wang, Pu Zhao, Hao Tang, Stratis Ioannidis, Yanzhi Wang
To address these issues, we propose a framework that adaptively selects and aggregates knowledge from diverse LLMs to build a single, stronger model, avoiding the high memory overhead of ensemble and inflexible weight merging.
1 code implementation • 23 May 2025 • Mingrui Wu, Lu Wang, Pu Zhao, Fangkai Yang, Jianjin Zhang, Jianfeng Liu, Yuefeng Zhan, Weihao Han, Hao Sun, Jiayi Ji, Xiaoshuai Sun, QIngwei Lin, Weiwei Deng, Dongmei Zhang, Feng Sun, Qi Zhang, Rongrong Ji
Instead of relying on handcrafted rules or stylistic rewrites, our method trains a language model to generate structured, self-reflective prompts by optimizing for image-level outcomes.
1 code implementation • 23 May 2025 • Lin Zhao, Yushu Wu, Xinru Jiang, Jianyang Gu, Yanzhi Wang, Xiaolin Xu, Pu Zhao, Xue Lin
Specifically, we adopt DDIM inversion to map the latents of the full dataset from a low-normality latent domain to a high-normality Gaussian domain, preserving information and ensuring structural consistency to generate representative latents for the distilled dataset.
1 code implementation • 23 May 2025 • Zhenglun Kong, Yize Li, Fanhu Zeng, Lei Xin, Shvat Messica, Xue Lin, Pu Zhao, Manolis Kellis, Hao Tang, Marinka Zitnik
We highlight its potential to drive new model architectures and learning strategies that improve robustness, increase interpretability, and better align with the objectives of generative modeling.
no code implementations • 20 May 2025 • Jun Liu, Zhenglun Kong, Peiyan Dong, Changdi Yang, Tianqi Li, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Pu Zhao, Xue Lin, Dong Huang, Yanzhi Wang
Large language models (LLMs) exhibit strong capabilities as decision-making agents by interleaving reasoning and actions, as seen in ReAct-style frameworks.
1 code implementation • 17 May 2025 • Xuan Shen, Weize Ma, Yufa Zhou, Enhao Tang, Yanyue Xie, Zhengang Li, Yifan Gong, Quanyi Wang, Henghui Ding, Yiwei Wang, Yanzhi Wang, Pu Zhao, Jun Lin, Jiuxiang Gu
In this paper, we propose the \textbf{FastCar} framework to accelerate the decode phase for the AR video generation by exploring the temporal redundancy.
1 code implementation • 17 May 2025 • Xuan Shen, Chenxia Han, Yufa Zhou, Yanyue Xie, Yifan Gong, Quanyi Wang, Yiwei Wang, Yanzhi Wang, Pu Zhao, Jiuxiang Gu
To address this, we propose the DraftAttention, a training-free framework for the acceleration of video diffusion transformers with dynamic sparse attention on GPUs.
1 code implementation • 20 Apr 2025 • Chaoyun Zhang, He Huang, Chiming Ni, Jian Mu, Si Qin, Shilin He, Lu Wang, Fangkai Yang, Pu Zhao, Chao Du, Liqun Li, Yu Kang, Zhao Jiang, Suzhen Zheng, Rujia Wang, Jiaxu Qian, Minghua Ma, Jian-Guang Lou, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
Recent Computer-Using Agents (CUAs), powered by multimodal large language models (LLMs), offer a promising direction for automating complex desktop workflows through natural language.
1 code implementation • CVPR 2025 • Xuan Shen, Weize Ma, Jing Liu, Changdi Yang, Rui Ding, Quanyi Wang, Henghui Ding, Wei Niu, Yanzhi Wang, Pu Zhao, Jun Lin, Jiuxiang Gu
Monocular Depth Estimation (MDE) has emerged as a pivotal task in computer vision, supporting numerous real-world applications.
no code implementations • 24 Feb 2025 • Chenghua Huang, Lu Wang, Fangkai Yang, Pu Zhao, Zhixu Li, QIngwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
Proximal Policy Optimization (PPO)-based Reinforcement Learning from Human Feedback (RLHF) is essential for aligning large language models (LLMs) with human preferences.
1 code implementation • 31 Jan 2025 • Xuan Shen, Yizhou Wang, Xiangxi Shi, Yanzhi Wang, Pu Zhao, Jiuxiang Gu
Meanwhile, we design corresponding Heima Decoder with traditional Large Language Models (LLMs) to adaptively interpret the hidden representations into variable-length textual sequence, reconstructing reasoning processes that closely resemble the original CoTs.
no code implementations • 27 Jan 2025 • Xing Zhang, Jiaheng Wen, Fangkai Yang, Pu Zhao, Yu Kang, Junhao Wang, Maoquan Wang, Yufan Huang, Elsie Nallipogu, QIngwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages.
no code implementations • 8 Jan 2025 • Jun Liu, Zhenglun Kong, Peiyan Dong, Changdi Yang, Xuan Shen, Pu Zhao, Hao Tang, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang
Although Low-Rank Adaptation (LoRA) is widely used and effective for fine-tuning, we have observed that its scaling factor can limit or even reduce performance as the rank size increases.
no code implementations • 23 Dec 2024 • Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges for data collection and annotation.
no code implementations • 17 Dec 2024 • Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Jing Liu, Ruiyi Zhang, Ryan A. Rossi, Hao Tan, Tong Yu, Xiang Chen, Yufan Zhou, Tong Sun, Pu Zhao, Yanzhi Wang, Jiuxiang Gu
Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing.
no code implementations • 17 Dec 2024 • Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Yanyu Li, Yifan Gong, Kai Zhang, Hao Tan, Jason Kuen, Henghui Ding, Zhihao Shu, Wei Niu, Pu Zhao, Yanzhi Wang, Jiuxiang Gu
In this paper, we show that performing the full computation of the model at each diffusion step is unnecessary, as some computations can be skipped by lazily reusing the results of previous steps.
1 code implementation • 13 Dec 2024 • Lu Wang, Fangkai Yang, Chaoyun Zhang, Junting Lu, Jiaxu Qian, Shilin He, Pu Zhao, Bo Qiao, Ray Huang, Si Qin, Qisheng Su, Jiayi Ye, Yudi Zhang, Jian-Guang Lou, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actions.
no code implementations • 8 Dec 2024 • Jingxu Ng, Cheng Lv, Pu Zhao, Wei Niu, Juyi Lin, Minzhou Pan, Yun Liang, Yanzhi Wang
To address this, stable-diffusion. cpp (Sdcpp) emerges as an efficient inference framework to accelerate the diffusion models.
1 code implementation • 8 Dec 2024 • Pu Zhao, Xuan Shen, Zhenglun Kong, Yixin Shen, Sung-En Chang, Timothy Rupprecht, Lei Lu, Enfu Nan, Changdi Yang, Yumei He, Xingchen Xu, Yu Huang, Wei Wang, Yue Chen, Yong He, Yanzhi Wang
Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities.
1 code implementation • 2 Nov 2024 • Zheng Zhan, Yushu Wu, Yifan Gong, Zichong Meng, Zhenglun Kong, Changdi Yang, Geng Yuan, Pu Zhao, Wei Niu, Yanzhi Wang
The rapid progress in artificial intelligence-generated content (AIGC), especially with diffusion models, has significantly advanced development of high-quality video generation.
1 code implementation • 1 Nov 2024 • Chenghua Huang, Zhizhen Fan, Lu Wang, Fangkai Yang, Pu Zhao, Zeqi Lin, QIngwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for aligning language models with human preferences, playing a pivotal role in the success of conversational models like GPT-4, ChatGPT, and Llama 2.
1 code implementation • 21 Oct 2024 • Pu Zhao, Fei Sun, Xuan Shen, Pinrui Yu, Zhenglun Kong, Yanzhi Wang, Xue Lin
To deal with this problem, post-training pruning methods are proposed to prune LLMs in one-shot without retraining.
1 code implementation • 16 Oct 2024 • Zheng Zhan, Yushu Wu, Zhenglun Kong, Changdi Yang, Yifan Gong, Xuan Shen, Xue Lin, Pu Zhao, Yanzhi Wang
While token reduction techniques offer a straightforward post-training strategy, we find that applying existing methods directly to SSMs leads to substantial performance drops.
1 code implementation • 1 Oct 2024 • Yifan Gong, Yushu Wu, Zheng Zhan, Pu Zhao, Liangkai Liu, Chao Wu, Xulong Tang, Yanzhi Wang
Two-stage object detectors exhibit high accuracy and precise localization, especially for identifying small objects that are favorable for various edge applications.
no code implementations • 27 Sep 2024 • Zheng Zhan, Zhenglun Kong, Yifan Gong, Yushu Wu, Zichong Meng, Hangyu Zheng, Xuan Shen, Stratis Ioannidis, Wei Niu, Pu Zhao, Yanzhi Wang
Inspired by the observations that the final prediction in vision transformers (ViTs) is only based on a subset of most informative tokens, we take the novel step of enhancing the efficiency of SSM-based vision models through token-based pruning.
1 code implementation • 25 Sep 2024 • Xuan Shen, Pu Zhao, Yifan Gong, Zhenglun Kong, Zheng Zhan, Yushu Wu, Ming Lin, Chao Wu, Xue Lin, Yanzhi Wang
Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research.
1 code implementation • 1 Aug 2024 • Mengkang Hu, Pu Zhao, Can Xu, Qingfeng Sun, JianGuang Lou, QIngwei Lin, Ping Luo, Saravan Rajmohan
Moreover, to increase the difficulty diversity of generated planning tasks, we propose a bidirectional evolution method, Bi-Evol, that evolves planning tasks from easier and harder directions to synthesize a task set with a smoother difficulty curve.
no code implementations • 15 Jul 2024 • Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, QIngwei Lin, JianGuang Lou, Shifeng Chen, Yansong Tang, Weizhu Chen
In this paper, we introduce Arena Learning, an innovative offline strategy designed to simulate these arena battles using AI-driven annotations to evaluate battle outcomes, thus facilitating the continuous improvement of the target model through both supervised fine-tuning and reinforcement learning.
no code implementations • 19 Jun 2024 • Kaikai An, Fangkai Yang, Liqun Li, Junting Lu, Sitao Cheng, Shuzheng Si, Lu Wang, Pu Zhao, Lele Cao, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang, Baobao Chang
Recent advances in retrieval-augmented generation have significantly improved the performance of question-answering systems, particularly on factoid '5Ws' questions.
no code implementations • 16 Mar 2024 • Jun Liu, Zhenglun Kong, Pu Zhao, Changdi Yang, Hao Tang, Xuan Shen, Geng Yuan, Wei Niu, Wenbin Zhang, Xue Lin, Dong Huang, Yanzhi Wang
For example, HyWIA surpasses the cutting-edge LLM-Pruner by an average margin of 2. 82% in accuracy across seven downstream tasks when pruning LLaMA-7B by 50%.
no code implementations • 8 Mar 2024 • Zichong Meng, Changdi Yang, Jun Liu, Hao Tang, Pu Zhao, Yanzhi Wang
In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-context learning capability and unifying language instruction.
no code implementations • 8 Mar 2024 • Zichong Meng, Jie Zhang, Changdi Yang, Zheng Zhan, Pu Zhao, Yanzhi Wang
On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data.
no code implementations • 27 Feb 2024 • Kaikai An, Fangkai Yang, Junting Lu, Liqun Li, Zhixing Ren, Hao Huang, Lu Wang, Pu Zhao, Yu Kang, Hua Ding, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Effective incident management is pivotal for the smooth operation of enterprises-level cloud services.
1 code implementation • 16 Feb 2024 • Xuan Shen, Zhenglun Kong, Changdi Yang, Zhaoyang Han, Lei Lu, Peiyan Dong, Cheng Lyu, Chih-hsiang Li, Xuehang Guo, Zhihao Shu, Wei Niu, Miriam Leeser, Pu Zhao, Yanzhi Wang
In this paper, we propose EdgeQAT, the Entropy and Distribution Guided QAT for the optimization of lightweight LLMs to achieve inference acceleration on Edge devices.
no code implementations • 30 Jan 2024 • Chenan Wang, Pu Zhao, Siyue Wang, Xue Lin
Deep Neural Network (DNN) models when implemented on executing devices as the inference engines are susceptible to Fault Injection Attacks (FIAs) that manipulate model parameters to disrupt inference execution with disastrous performance.
no code implementations • 13 Jan 2024 • Lu Wang, Chao Du, Pu Zhao, Chuan Luo, Zhangchi Zhu, Bo Qiao, Wei zhang, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
To correct the negative sampling bias, we propose a novel contrastive learning method named Positive-Unlabeled Contrastive Learning (PUCL).
no code implementations • 8 Jan 2024 • Haozhe Li, Minghua Ma, Yudong Liu, Pu Zhao, Lingling Zheng, Ze Li, Yingnong Dang, Murali Chintalapati, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
Using two real-world datasets of disk failure prediction and conducting node prediction experiments in Microsoft Azure, which is a top-tier cloud provider that serves millions of users, we demonstrate Uptake can significantly improve the failure prediction accuracy by 5% on average.
1 code implementation • 29 Nov 2023 • Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang, Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin, Xiaoting Qin, Chao Du, Yong Xu, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
no code implementations • 24 Oct 2023 • Zezhong Wang, Fangkai Yang, Lu Wang, Pu Zhao, Hongru Wang, Liang Chen, QIngwei Lin, Kam-Fai Wong
Currently, there are two main approaches to address jailbreak attacks: safety training and safeguards.
1 code implementation • 18 Aug 2023 • Haipeng Luo, Qingfeng Sun, Can Xu, Pu Zhao, JianGuang Lou, Chongyang Tao, Xiubo Geng, QIngwei Lin, Shifeng Chen, Yansong Tang, Dongmei Zhang
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning.
Ranked #54 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
no code implementations • 3 Aug 2023 • Fangkai Yang, Wenjie Yin, Lu Wang, Tianci Li, Pu Zhao, Bo Liu, Paul Wang, Bo Qiao, Yudong Liu, Mårten Björkman, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
However, they suffer from poor data quality like data missing in model training and prediction, which limits the performance.
1 code implementation • 1 Aug 2023 • Zhangchi Zhu, Lu Wang, Pu Zhao, Chao Du, Wei zhang, Hang Dong, Bo Qiao, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
To mitigate the impact of label uncertainty and improve the robustness of learning with positive and unlabeled data, we propose a new robust PU learning method with a training strategy motivated by the nature of human learning: easy cases should be learned first.
4 code implementations • 14 Jun 2023 • Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #8 on
Code Generation
on CodeContests
1 code implementation • 19 May 2023 • Fangkai Yang, Pu Zhao, Zezhong Wang, Lu Wang, Jue Zhang, Mohit Garg, QIngwei Lin, Saravan Rajmohan, Dongmei Zhang
Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average due to its lack of specific domain knowledge.
no code implementations • 19 May 2023 • Liting Chen, Lu Wang, Hang Dong, Yali Du, Jie Yan, Fangkai Yang, Shuang Li, Pu Zhao, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang
The emergence of large language models (LLMs) has substantially influenced natural language processing, demonstrating exceptional results across various tasks.
1 code implementation • 8 May 2023 • Ziyang Luo, Can Xu, Pu Zhao, Xiubo Geng, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
4 code implementations • 24 Apr 2023 • Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
no code implementations • 23 Feb 2023 • Yize Li, Pu Zhao, Xue Lin, Bhavya Kailkhura, Ryan Goldhahn
Deep neural networks (DNNs) are sensitive to adversarial examples, resulting in fragile and unreliable performance in the real world.
1 code implementation • 6 Feb 2023 • Ziyang Luo, Pu Zhao, Can Xu, Xiubo Geng, Tao Shen, Chongyang Tao, Jing Ma, Qingwen Lin, Daxin Jiang
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
1 code implementation • ICCV 2023 • Ziyang Luo, Pu Zhao, Can Xu, Xiubo Geng, Tao Shen, Chongyang Tao, Jing Ma, QIngwei Lin, Daxin Jiang
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
no code implementations • CVPR 2023 • Changdi Yang, Pu Zhao, Yanyu Li, Wei Niu, Jiexiong Guan, Hao Tang, Minghai Qin, Bin Ren, Xue Lin, Yanzhi Wang
With the ever-increasing popularity of edge devices, it is necessary to implement real-time segmentation on the edge for autonomous driving and many other applications.
no code implementations • 9 Dec 2022 • Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang
By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.
1 code implementation • 10 Nov 2022 • Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, QIngwei Lin
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
1 code implementation • 8 Oct 2022 • Yihua Zhang, Yuguang Yao, Parikshit Ram, Pu Zhao, Tianlong Chen, Mingyi Hong, Yanzhi Wang, Sijia Liu
To reduce the computation overhead, various efficient 'one-shot' pruning methods have been developed, but these schemes are usually unable to find winning tickets as good as IMP.
no code implementations • 26 Sep 2022 • Hao Cheng, Pu Zhao, Yize Li, Xue Lin, James Diffenderfer, Ryan Goldhahn, Bhavya Kailkhura
Recently, Diffenderfer and Kailkhura proposed a new paradigm for learning compact yet highly accurate binary neural networks simply by pruning and quantizing randomly weighted full precision neural networks.
1 code implementation • 25 Jul 2022 • Yushu Wu, Yifan Gong, Pu Zhao, Yanyu Li, Zheng Zhan, Wei Niu, Hao Tang, Minghai Qin, Bin Ren, Yanzhi Wang
Instead of measuring the speed on mobile devices at each iteration during the search process, a speed model incorporated with compiler optimizations is leveraged to predict the inference latency of the SR block with various width configurations for faster convergence.
1 code implementation • 2 Jun 2022 • Yanyu Li, Pu Zhao, Geng Yuan, Xue Lin, Yanzhi Wang, Xin Chen
By combining the structural reparameterization and PaS, we successfully searched out a new family of VGG-like and lightweight networks, which enable the flexibility of arbitrary width with respect to each layer instead of each stage.
no code implementations • 22 Nov 2021 • Yifan Gong, Geng Yuan, Zheng Zhan, Wei Niu, Zhengang Li, Pu Zhao, Yuxuan Cai, Sijia Liu, Bin Ren, Xue Lin, Xulong Tang, Yanzhi Wang
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.
no code implementations • ICCV 2021 • Zheng Zhan, Yifan Gong, Pu Zhao, Geng Yuan, Wei Niu, Yushu Wu, Tianyun Zhang, Malith Jayaweera, David Kaeli, Bin Ren, Xue Lin, Yanzhi Wang
Though recent years have witnessed remarkable progress in single image super-resolution (SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep learning methods are confronted with the computation and memory consumption issues in practice, especially for resource-limited platforms such as mobile devices.
no code implementations • 28 Jun 2021 • Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Bin Ren, Yanzhi Wang, Xue Lin
Object detection plays an important role in self-driving cars for security development.
no code implementations • 30 May 2021 • Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang
In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model to meet both resource and real-time specifications of mobile devices.
no code implementations • 14 May 2021 • Siyue Wang, Xiao Wang, Pin-Yu Chen, Pu Zhao, Xue Lin
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
no code implementations • 26 Dec 2020 • Pu Zhao, Wei Niu, Geng Yuan, Yuxuan Cai, Hsin-Hsuan Sung, Sijia Liu, Xipeng Shen, Bin Ren, Yanzhi Wang, Xue Lin
3D object detection is an important task, especially in the autonomous driving application domain.
no code implementations • CVPR 2021 • Zhengang Li, Geng Yuan, Wei Niu, Pu Zhao, Yanyu Li, Yuxuan Cai, Xuan Shen, Zheng Zhan, Zhenglun Kong, Qing Jin, Zhiyu Chen, Sijia Liu, Kaiyuan Yang, Bin Ren, Yanzhi Wang, Xue Lin
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
no code implementations • 29 Sep 2020 • Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin, Sijia Liu
The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.
no code implementations • 15 Sep 2020 • Wei Niu, Zhenglun Kong, Geng Yuan, Weiwen Jiang, Jiexiong Guan, Caiwen Ding, Pu Zhao, Sijia Liu, Bin Ren, Yanzhi Wang
Our framework can guarantee the identified model to meet both resource and real-time specifications of mobile devices, thus achieving real-time execution of large transformer-based models like BERT variants.
3 code implementations • ICLR 2020 • Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin
In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
no code implementations • 22 Apr 2020 • Wei Niu, Pu Zhao, Zheng Zhan, Xue Lin, Yanzhi Wang, Bin Ren
High-end mobile platforms rapidly serve as primary computing devices for a wide range of Deep Neural Network (DNN) applications.
no code implementations • 26 Feb 2020 • Hao Cheng, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, Xue Lin
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.
1 code implementation • 18 Feb 2020 • Pu Zhao, Pin-Yu Chen, Siyue Wang, Xue Lin
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability.
no code implementations • 23 Jan 2020 • Xiaolong Ma, Zhengang Li, Yifan Gong, Tianyun Zhang, Wei Niu, Zheng Zhan, Pu Zhao, Jian Tang, Xue Lin, Bin Ren, Yanzhi Wang
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem.
1 code implementation • ICCV 2019 • Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations.
no code implementations • 28 May 2019 • Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, Xue Lin
Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters.
no code implementations • 3 Apr 2019 • Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, Xue Lin
It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.
no code implementations • 13 Sep 2018 • Siyue Wang, Xiao Wang, Pu Zhao, Wujie Wen, David Kaeli, Peter Chin, Xue Lin
Based on the observations of the effect of test dropout rate on test accuracy and attack success rate, we propose a defensive dropout algorithm to determine an optimal test dropout rate given the neural network model and the attacker's strategy for generating adversarial examples. We also investigate the mechanism behind the outstanding defense effects achieved by the proposed defensive dropout.
1 code implementation • ICLR 2019 • Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, huan zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.
no code implementations • 9 Apr 2018 • Pu Zhao, Sijia Liu, Yanzhi Wang, Xue Lin
In the literature, the added distortions are usually measured by L0, L1, L2, and L infinity norms, namely, L0, L1, L2, and L infinity attacks, respectively.