Search Results for author: Xing Hu

Found 39 papers, 12 papers with code

Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splitting

1 code implementation17 Dec 2023 Dawei Yang, Ning He, Xing Hu, Zhihang Yuan, Jiangyong Yu, Chen Xu, Zhe Jiang

Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources.


Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?

no code implementations30 Nov 2023 Zhengyue Zhao, Jinhao Duan, Kaidi Xu, Chenan Wang, Rui Zhangp Zidong Dup Qi Guo, Xing Hu

Although these studies have demonstrated the ability to protect images, it is essential to consider that these methods may not be entirely applicable in real-world scenarios.

EditSum: A Retrieve-and-Edit Framework for Source Code Summarization

no code implementations26 Aug 2023 Jia Li, Yongmin Li, Ge Li, Xing Hu, Xin Xia, Zhi Jin

Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code.

Code Summarization Informativeness +1

Pushing the Limits of Machine Design: Automated CPU Design with AI

1 code implementation21 Jun 2023 Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen

By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours.

Online Prototype Alignment for Few-shot Policy Transfer

1 code implementation12 Jun 2023 Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Yunkai Gao, Kaizhao Yuan, Ruizhi Chen, Siming Lan, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen

Domain adaptation in reinforcement learning (RL) mainly deals with the changes of observation when transferring the policy to a new environment.

Domain Adaptation Reinforcement Learning (RL)

Flew Over Learning Trap: Learn Unlearnable Samples by Progressive Staged Training

1 code implementation3 Jun 2023 Pucheng Dang, Xing Hu, Kaidi Xu, Jinhao Duan, Di Huang, Husheng Han, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen

Unlearning techniques are proposed to prevent third parties from exploiting unauthorized data, which generate unlearnable samples by adding imperceptible perturbations to data for public publishing.

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

no code implementations2 Jun 2023 Zhengyue Zhao, Jinhao Duan, Xing Hu, Kaidi Xu, Chenan Wang, Rui Zhang, Zidong Du, Qi Guo, Yunji Chen

This imperceptible protective noise makes the data almost unlearnable for diffusion models, i. e., diffusion models trained or fine-tuned on the protected data cannot generate high-quality and diverse images related to the protected training data.

Denoising Image Generation

ANPL: Towards Natural Programming with Interactive Decomposition

1 code implementation NeurIPS 2023 Di Huang, Ziyuan Nan, Xing Hu, Pengwei Jin, Shaohui Peng, Yuanbo Wen, Rui Zhang, Zidong Du, Qi Guo, Yewen Pu, Yunji Chen

We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together.

Code Generation Program Synthesis

Conceptual Reinforcement Learning for Language-Conditioned Tasks

no code implementations9 Mar 2023 Shaohui Peng, Xing Hu, Rui Zhang, Jiaming Guo, Qi Yi, Ruizhi Chen, Zidong Du, Ling Li, Qi Guo, Yunji Chen

Recently, the language-conditioned policy is proposed to facilitate policy transfer through learning the joint representation of observation and text that catches the compact and invariant information across environments.

reinforcement-learning Reinforcement Learning (RL)

Ultra-low Precision Multiplication-free Training for Deep Neural Networks

no code implementations28 Feb 2023 Chang Liu, Rui Zhang, Xishan Zhang, Yifan Hao, Zidong Du, Xing Hu, Ling Li, Qi Guo

The energy-efficient works try to decrease the precision of multiplication or replace the multiplication with energy-efficient operations such as addition or bitwise shift, to reduce the energy consumption of FP32 multiplications.


Online Symbolic Regression with Informative Query

no code implementations21 Feb 2023 Pengwei Jin, Di Huang, Rui Zhang, Xing Hu, Ziyuan Nan, Zidong Du, Qi Guo, Yunji Chen

Symbolic regression, the task of extracting mathematical expressions from the observed data $\{ \vx_i, y_i \}$, plays a crucial role in scientific discovery.

regression Symbolic Regression

LGN-Net: Local-Global Normality Network for Video Anomaly Detection

1 code implementation14 Nov 2022 Mengyang Zhao, Xinhua Zeng, Yang Liu, Jing Liu, Di Li, Xing Hu, Chengxin Pang

Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies.

Anomaly Detection Video Anomaly Detection

CodeEditor: Learning to Edit Source Code with Pre-trained Models

1 code implementation31 Oct 2022 Jia Li, Ge Li, Zhuo Li, Zhi Jin, Xing Hu, Kechi Zhang, Zhiyi Fu

Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task.

Language Modelling Masked Language Modeling

Poison Attack and Defense on Deep Source Code Processing Models

no code implementations31 Oct 2022 Jia Li, Zhuo Li, Huangzhao Zhang, Ge Li, Zhi Jin, Xing Hu, Xin Xia

The attackers aim to inject insidious backdoors into models by poisoning the training data with poison samples.

Clone Detection Code Repair +1

Object-Category Aware Reinforcement Learning

no code implementations13 Oct 2022 Qi Yi, Rui Zhang, Shaohui Peng, Jiaming Guo, Xing Hu, Zidong Du, Xishan Zhang, Qi Guo, Yunji Chen

Object-oriented reinforcement learning (OORL) is a promising way to improve the sample efficiency and generalization ability over standard RL.

Feature Engineering Object +3

Causality-driven Hierarchical Structure Discovery for Reinforcement Learning

no code implementations13 Oct 2022 Shaohui Peng, Xing Hu, Rui Zhang, Ke Tang, Jiaming Guo, Qi Yi, Ruizhi Chen, Xishan Zhang, Zidong Du, Ling Li, Qi Guo, Yunji Chen

To address this issue, we propose CDHRL, a causality-driven hierarchical reinforcement learning framework, leveraging a causality-driven discovery instead of a randomness-driven exploration to effectively build high-quality hierarchical structures in complicated environments.

Hierarchical Reinforcement Learning reinforcement-learning +1

Neural Program Synthesis with Query

no code implementations ICLR 2022 Di Huang, Rui Zhang, Xing Hu, Xishan Zhang, Pengwei Jin, Nan Li, Zidong Du, Qi Guo, Yunji Chen

In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space.

Program Synthesis

Toward Robust Spiking Neural Network Against Adversarial Perturbation

no code implementations12 Apr 2022 Ling Liang, Kaidi Xu, Xing Hu, Lei Deng, Yuan Xie

To the best of our knowledge, this is the first analysis on robust training of SNNs.

Information retrieval for label noise document ranking by bag sampling and group-wise loss

no code implementations12 Mar 2022 Chunyu Li, Jiajia Ding, Xing Hu, Fan Wang

To fit bag sampling well, after query and document are encoded, the global features of each group are extracted by convolutional layer and max-pooling to improve the model's resistance to the impact of labeling noise, finally, calculate the LCE group-wise loss.

Document Ranking Information Retrieval +2

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

no code implementations7 Mar 2022 Ben Fei, Weidong Yang, Wenming Chen, Zhijun Li, Yikang Li, Tao Ma, Xing Hu, Lipeng Ma

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision.

Point Cloud Completion

ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

no code implementations NeurIPS 2021 Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling Liang, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen

Our experimental results show that the certified accuracy is increased from 36. 3% (the state-of-the-art certified detection) to 60. 4% on the ImageNet dataset, largely pushing the certified defenses for practical use.

Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment

1 code implementation26 Jul 2021 Jiaming Guo, Rui Zhang, Xishan Zhang, Shaohui Peng, Qi Yi, Zidong Du, Xing Hu, Qi Guo, Yunji Chen

In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments.

Policy Gradient Methods

Rubik: A Hierarchical Architecture for Efficient Graph Learning

no code implementations26 Sep 2020 Xiaobing Chen, yuke wang, Xinfeng Xie, Xing Hu, Abanti Basak, Ling Liang, Mingyu Yan, Lei Deng, Yufei Ding, Zidong Du, Yunji Chen, Yuan Xie

Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.

Hardware Architecture

HyGCN: A GCN Accelerator with Hybrid Architecture

1 code implementation7 Jan 2020 Mingyu Yan, Lei Deng, Xing Hu, Ling Liang, Yujing Feng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, Yuan Xie

In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU.

Distributed, Parallel, and Cluster Computing

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

no code implementations1 Jan 2020 Ling Liang, Xing Hu, Lei Deng, Yujie Wu, Guoqi Li, Yufei Ding, Peng Li, Yuan Xie

Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models accurately given Spatio-temporal gradient maps.

Adversarial Attack

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

1 code implementation3 Nov 2019 Lei Deng, Yujie Wu, Yifan Hu, Ling Liang, Guoqi Li, Xing Hu, Yufei Ding, Peng Li, Yuan Xie

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency.

Model Compression Quantization

Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

no code implementations10 Mar 2019 Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, Yuan Xie

As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject.

Cryptography and Security Hardware Architecture

FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture

no code implementations28 Jan 2019 Yu Ji, Youyang Zhang, Xinfeng Xie, Shuangchen Li, Peiqi Wang, Xing Hu, Youhui Zhang, Yuan Xie

In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing.

Programmable Neural Network Trojan for Pre-Trained Feature Extractor

no code implementations23 Jan 2019 Yu Ji, Zixin Liu, Xing Hu, Peiqi Wang, Youhui Zhang

Existing studies have explored the outsourced training attack scenario and transfer learning attack scenario in some small datasets for specific domains, with limited numbers of fixed target classes.

Transfer Learning

Batch Normalization Sampling

no code implementations25 Oct 2018 Zhaodong Chen, Lei Deng, Guoqi Li, Jiawei Sun, Xing Hu, Xin Ma, Yuan Xie

In this paper, we propose alleviating this problem through sampling only a small fraction of data for normalization at each iteration.

Computational Efficiency

Dynamic Sparse Graph for Efficient Deep Learning

no code implementations ICLR 2019 Liu Liu, Lei Deng, Xing Hu, Maohua Zhu, Guoqi Li, Yufei Ding, Yuan Xie

We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference.

Dimensionality Reduction

Crossbar-aware neural network pruning

no code implementations25 Jul 2018 Ling Liang, Lei Deng, Yueling Zeng, Xing Hu, Yu Ji, Xin Ma, Guoqi Li, Yuan Xie

Crossbar architecture based devices have been widely adopted in neural network accelerators by taking advantage of the high efficiency on vector-matrix multiplication (VMM) operations.

Network Pruning

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