Search Results for author:
Found 10 papers, 5 papers with code
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions.
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses.
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Despite advances in AI alignment, language models (LM) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries modify input prompts to induce harmful behavior.
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
We propose a conceptually simple and lightweight framework for improving the robustness of vision models through the combination of knowledge distillation and data augmentation.
Ranked #13 on
Domain Generalization
on ImageNet-Sketch
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents.
Ranked #7 on
Code Generation
on MBPP
A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance
Domain generalization studies the problem of training a model with samples from several domains (or distributions) and then testing the model with samples from a new, unseen domain.
Ranked #15 on
Domain Generalization
on PACS
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task.
Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing
Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change.
Analysis of Contraction Effort Level in EMG-Based Gesture Recognition Using Hyperdimensional Computing
Varying contraction levels of muscles is a big challenge in electromyography-based gesture recognition.
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
