Search Results for author:
Found 19 papers, 6 papers with code
Equilibria and Their Stability Do Not Depend on the Control Barrier Function in Safe Optimization-Based Control
While this approach effectively guarantees safety for a given CBF, the CBF-based control law can introduce undesirable equilibrium points (i. e., points that are not equilibria of the original system); open questions remain on how the choice of CBF influences the number and locations of undesirable equilibria and, in general, the dynamics of the closed-loop system.
Characterization of the Dynamical Properties of Safety Filters for Linear Planar Systems
We provide a sufficient and necessary condition for the existence of undesirable equilibria and show that the Jacobian matrix of the closed-loop system evaluated at an undesirable equilibrium always has a nonpositive eigenvalue.
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
This approach enables agents to form a global consensus from local observations, using it as an additional piece of information to guide collaborative actions during execution.
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-of-the-art performance.
Ranked #23 on
Rgb-T Tracking
on RGBT234
Optimal Power Flow Pursuit via Feedback-based Safe Gradient Flow
This paper considers the problem of controlling inverter-interfaced distributed energy resources (DERs) in a distribution grid to solve an AC optimal power flow (OPF) problem in real time.
Online Regulation of Dynamical Systems to Solutions of Constrained Optimization Problems
This paper considers the problem of regulating a dynamical system to equilibria that are defined as solutions of an input- and state-constrained optimization problem.
Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory
In this paper, we expand the concept of equivalent feature and provide the definition of what we call functionally equivalent features.
The Emergence of Economic Rationality of GPT
As large language models (LLMs) like GPT become increasingly prevalent, it is essential that we assess their capabilities beyond language processing.
Energy-based Out-of-Distribution Detection for Graph Neural Networks
This paves a way for a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Multi-Task System Identification of Similar Linear Time-Invariant Dynamical Systems
This paper presents a system identification framework -- inspired by multi-task learning -- to estimate the dynamics of a given number of linear time-invariant (LTI) systems jointly by leveraging structural similarities across the systems.
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain
In this work, we introduce Shapley value, a metric of cooperative game theory, into the frequency domain and propose to quantify the positive (negative) impact of every frequency component of data on CNNs.
Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system.
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.
Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations
Medical ultrasound has become a routine examination approach nowadays and is widely adopted for different medical applications, so it is desired to have a robotic ultrasound system to perform the ultrasound scanning autonomously.
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK
Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network.
A Unified Game-Theoretic Interpretation of Adversarial Robustness
This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.
Technical Note: Game-Theoretic Interactions of Different Orders
In this study, we define interaction components of different orders between two input variables based on game theory.
Rotation-Equivariant Neural Networks for Privacy Protection
Compared to the traditional neural network, the RENN uses d-ary vectors/tensors as features, in which each element is a d-ary number.
