no code implementations • ICML 2020 • Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
In this paper, we study the robustness verification and defense with respect to general $\ell_p$ norm perturbation for ensemble trees and stumps.
no code implementations • 15 Apr 2024 • Yihan Wang, Lujun Zhang
The merged results are compared with the widely used reanalysis precipitation product, Multi-Radar Multi-Sensor (MRMS), and assessed against gauge observational data from the California Data Exchange Center (CDEC).
no code implementations • 11 Mar 2024 • Suqi Li, Yihan Wang, Bailu Wang, Giorgio Battistelli, Luigi Chisci, Guolong Cui
Since RM and DFM are induced by the same motion parameters, existing approaches such as the generalized Radon-Fourier transform (GRFT) or the keystone transform (KT)-matching filter process (MFP) adopt the same search space for the motion parameters in order to eliminate both effects, thus leading to large redundancy in computation.
no code implementations • 27 Feb 2024 • Zishun Zheng, Yihan Wang, Yuan Lin
This approach divides the driving control of vehicles on highways into two parts: lane-change decision and lane-change control, both of which are solved using the MPC method.
1 code implementation • 26 Feb 2024 • Yihan Wang, Zhouxing Shi, Andrew Bai, Cho-Jui Hsieh
The inferred prompt is called the backtranslated prompt which tends to reveal the actual intent of the original prompt, since it is generated based on the LLM's response and is not directly manipulated by the attacker.
no code implementations • 6 Feb 2024 • Yihan Wang, Yifan Zhu, Xiao-Shan Gao
Availability attacks can prevent the unauthorized use of private data and commercial datasets by generating imperceptible noise and making unlearnable examples before release.
1 code implementation • 31 Jan 2024 • Shuang Liu, Yihan Wang, Xiao-Shan Gao
Unlearnable example attacks are data poisoning attacks aiming to degrade the clean test accuracy of deep learning by adding imperceptible perturbations to the training samples, which can be formulated as a bi-level optimization problem.
no code implementations • 6 Jan 2024 • Yihan Wang, Shuang Liu, Xiao-Shan Gao
Stability analysis is an essential aspect of studying the generalization ability of deep learning, as it involves deriving generalization bounds for stochastic gradient descent-based training algorithms.
no code implementations • 16 Nov 2023 • Yuhang Li, Yihan Wang, Zhouxing Shi, Cho-Jui Hsieh
In this work, we propose to improve the quality of texts generated by a watermarked language model by Watermarking with Importance Scoring (WIS).
no code implementations • 25 Oct 2023 • Qiao Yan, Yihan Wang
Additionally, we propose a pioneering backbone, the Radar Feature Assisted backbone, explicitly crafted to fully exploit the valuable Doppler velocity and reflectivity data provided by the 4D radar sensor.
no code implementations • 20 Aug 2023 • Qiao Yan, Yihan Wang
To validate the robustness of 4D radars and thermal cameras for 3D object detection in challenging weather conditions, we propose a new multi-modal fusion method called RTDF-RCNN, which leverages the complementary strengths of 4D radars and thermal cameras to boost object detection performance.
no code implementations • 21 Jul 2023 • Yihan Wang, Qiao Yan, Yi Wang
LiDAR-based 3D object detection is of paramount importance for autonomous driving.
no code implementations • 29 Jun 2023 • Yihan Wang, Lijia Yu, Xiao-Shan Gao
Invariance to spatial transformations such as translations and rotations is a desirable property and a basic design principle for classification neural networks.
1 code implementation • CVPR 2023 • Alexander Raistrick, Lahav Lipson, Zeyu Ma, Lingjie Mei, Mingzhe Wang, Yiming Zuo, Karhan Kayan, Hongyu Wen, Beining Han, Yihan Wang, Alejandro Newell, Hei Law, Ankit Goyal, Kaiyu Yang, Jia Deng
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
1 code implementation • 31 May 2023 • Ruining Deng, Yanwei Li, Peize Li, Jiacheng Wang, Lucas W. Remedios, Saydolimkhon Agzamkhodjaev, Zuhayr Asad, Quan Liu, Can Cui, Yaohong Wang, Yihan Wang, Yucheng Tang, Haichun Yang, Yuankai Huo
The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data.
2 code implementations • 31 May 2023 • Zhouxing Shi, Yihan Wang, Fan Yin, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh
The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
no code implementations • 1 Nov 2022 • Yihan Wang, Si Si, Daliang Li, Michal Lukasik, Felix Yu, Cho-Jui Hsieh, Inderjit S Dhillon, Sanjiv Kumar
Pretrained large language models (LLMs) are general purpose problem solvers applicable to a diverse set of tasks with prompts.
2 code implementations • 13 Oct 2022 • Zhouxing Shi, Yihan Wang, huan zhang, Zico Kolter, Cho-Jui Hsieh
In this paper, we develop an efficient framework for computing the $\ell_\infty$ local Lipschitz constant of a neural network by tightly upper bounding the norm of Clarke Jacobian via linear bound propagation.
1 code implementation • 25 May 2022 • Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, Zhiting Hu
RLPrompt formulates a parameter-efficient policy network that generates the desired discrete prompt after training with reward.
1 code implementation • CVPR 2022 • Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, Song Han
Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including Fusion Deconv Head and Large Kernel Convs.
Ranked #5 on Multi-Person Pose Estimation on MS COCO (Validation AP metric)
no code implementations • 20 Mar 2022 • Lijia Yu, Yihan Wang, Xiao-Shan Gao
In this paper, a new parameter perturbation attack on DNNs, called adversarial parameter attack, is proposed, in which small perturbations to the parameters of the DNN are made such that the accuracy of the attacked DNN does not decrease much, but its robustness becomes much lower.
no code implementations • ICLR 2022 • Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
Interval Bound Propagation (IBP) is so far the base of state-of-the-art methods for training neural networks with certifiable robustness guarantees when potential adversarial perturbations present, while the convergence of IBP training remains unknown in existing literature.
no code implementations • 29 Sep 2021 • huan zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J Zico Kolter
In this work, we formulate an adversarial attack using a branch-and-bound (BaB) procedure on ReLU neural networks and search adversarial examples in the activation space corresponding to binary variables in a mixed integer programming (MIP) formulation.
no code implementations • NAACL 2021 • Yihan Wang, Yutong Shao, Ndapa Nakashole
This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one.
2 code implementations • NeurIPS 2021 • Zhouxing Shi, Yihan Wang, huan zhang, JinFeng Yi, Cho-Jui Hsieh
Despite that state-of-the-art (SOTA) methods including interval bound propagation (IBP) and CROWN-IBP have per-batch training complexity similar to standard neural network training, they usually use a long warmup schedule with hundreds or thousands epochs to reach SOTA performance and are thus still costly.
no code implementations • ICLR 2021 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
3 code implementations • ICLR 2021 • Kaidi Xu, huan zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, Cho-Jui Hsieh
Formal verification of neural networks (NNs) is a challenging and important problem.
1 code implementation • 20 Aug 2020 • Yihan Wang, huan zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
In this paper, we study the problem of robustness verification and certified defense with respect to general $\ell_p$ norm perturbations for ensemble decision stumps and trees.
1 code implementation • 24 Jul 2020 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
5 code implementations • NeurIPS 2020 • Kaidi Xu, Zhouxing Shi, huan zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh
Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.