Search Results for author:
Found 16 papers, 1 papers with code
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Our experimental results demonstrate the superior performance of the MC Ensemble strategy in OOD detection compared to both the naive Deep Ensemble method and a standalone model of comparable size.
QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks
Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions among inputs and features.
Hardware Aware Neural Architecture Search
Neural Architecture Search
Leveraging Untrustworthy Commands for Multi-Robot Coordination in Unpredictable Environments: A Bandit Submodular Maximization Approach
MetaBSG leverages a meta-algorithm to learn whether the robots should follow the commands or a recently developed submodular coordination algorithm, Bandit Sequential Greedy (BSG) [1], which has performance guarantees even in unpredictable and partially-observable environments.
Bandit Submodular Maximization for Multi-Robot Coordination in Unpredictable and Partially Observable Environments
We are motivated by the future of autonomy that involves multiple robots coordinating actions in dynamic, unstructured, and partially observable environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control
In this paper, we enable projection-free online learning within the framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures how the history of decisions affects the current outcome by allowing the online learning loss functions to depend on both current and past decisions.
Online Submodular Coordination with Bounded Tracking Regret: Theory, Algorithm, and Applications to Multi-Robot Coordination
We are motivated by the future of autonomy that involves multiple robots coordinating in dynamic, unstructured, and adversarial environments to complete complex tasks such as target tracking, environmental mapping, and area monitoring.
Resource-Aware Distributed Submodular Maximization: A Paradigm for Multi-Robot Decision-Making
All robots can thus afford our algorithm, irrespective of their resources.
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration
The significant success of Deep Neural Networks (DNNs) is highly promoted by the multiple sophisticated DNN libraries.
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced Collaboration
In this paper, we propose Helios, a heterogeneity-aware FL framework to tackle the straggler issue.
Distributed, Parallel, and Cluster Computing
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications
Based on the detection result, we further propose a data recovery methodology to defend the physical adversarial attacks.
Censored Stable Subordinators and Fractional Derivatives
Based on the popular Caputo fractional derivative of order $\beta$ in $(0, 1)$, we define the censored fractional derivative on the positive half-line $\mathbb R_+$.
Classical Analysis and ODEs Probability 26A33, 60G52, 60G40
DoPa: A Comprehensive CNN Detection Methodology against Physical Adversarial Attacks
To address this issue, we propose DoPa -- a comprehensive CNN detection methodology for various physical adversarial attacks.
HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
In this work, we propose HASP -- a high-performance security enhancement approach to solve this security issue on mobile devices.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications. However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures.
