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Found 19 papers, 9 papers with code
DiffAtlas: GenAI-fying Atlas Segmentation via Image-Mask Diffusion
Accurate medical image segmentation is crucial for precise anatomical delineation.
AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge.
A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective
This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces.
MixLLM: Dynamic Routing in Mixed Large Language Models
Given mixed LLMs with their own strengths and weaknesses, LLM routing aims to identify the most suitable model for each query in the stream to maximize response quality and minimize cost and latency.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation
This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement.
BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models
Existing jailbreak strategies mainly focus on maximizing attack success rate (ASR), frequently neglecting other critical factors, including the relevance of the jailbreak response to the query and the level of stealthiness.
LCMDC: Large-scale Chinese Medical Dialogue Corpora for Automatic Triage and Medical Consultation
Experimental results on the LCMDC demonstrate the efficacy of our proposed systems.
Medical MLLM is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models
Security concerns related to Large Language Models (LLMs) have been extensively explored, yet the safety implications for Multimodal Large Language Models (MLLMs), particularly in medical contexts (MedMLLMs), remain insufficiently studied.
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality backgrounds and limited control over the synthetic output.
LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
This highlights the potential of Mamba in facilitating model lightweighting.
Knockoff-Guided Feature Selection via A Single Pre-trained Reinforced Agent
A deep Q-network, pre-trained with the original features and their corresponding pseudo labels, is employed to improve the efficacy of the exploration process in feature selection.
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
Additionally, we introduce graph relationship understanding and analysis functions into LLMs to enhance their focus on connectivity information in graph data.
Leveraging Frequency Domain Learning in 3D Vessel Segmentation
In this study, we leverage Fourier domain learning as a substitute for multi-scale convolutional kernels in 3D hierarchical segmentation models, which can reduce computational expenses while preserving global receptive fields within the network.
BiPFT: Binary Pre-trained Foundation Transformer with Low-rank Estimation of Binarization Residual Polynomials
Specifically, we first analyze the binarization error in self-attention operations and derive the polynomials of binarization error.
TD-Net: A Tri-domain network for sparse-view CT reconstruction
Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks, frequently suffers from image quality degradation, manifested as noise and artifacts.
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task.
Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception
Personalized chatbot-based teaching assistants can be crucial in addressing increasing classroom sizes, especially where direct teacher presence is limited.
