Search Results for author:
Found 13 papers, 7 papers with code
On Lp-norm Robustness of Ensemble Decision Stumps and Trees
In this paper, we study the robustness verification and defense with respect to general $\ell_p$ norm perturbation for ensemble trees and stumps.
RLPrompt: Optimizing Discrete Text Prompts With Reinforcement Learning
RLPrompt formulates a parameter-efficient policy network that generates the desired discrete prompt after training with reward.
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
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.
Adversarial Parameter Attack on Deep Neural Networks
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.
On the Convergence of Certified Robust Training with Interval Bound Propagation
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.
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
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.
Interactive Plot Manipulation using Natural Language
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.
Fast Certified Robust Training with Short Warmup
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.
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Formal verification of neural networks (NNs) is a challenging and important problem.
On $\ell_p$-norm Robustness of Ensemble Stumps and Trees
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.
Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond
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.
