Genre
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics Lukas Klein 1,2,3 Carsten Lüth
Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy.
Divide-and-Conquer Meets Consensus: Unleashing the Power of Functions in Code Generation
Recent work utilizes plan-and-solve decomposition to decrease the complexity and leverage self-tests to refine the generated program.
ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based Image Manipulation Y asheng Sun Yifan Yang
Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations.
Learning Interpretable Low-dimensional Representation via Physical Symmetry
However, most methods rely heavily on music domain knowledge.
Temporal Dynamic Quantization for Diffusion Models
The diffusion model has gained popularity in vision applications due to its remarkable generative performance and versatility.
Learning Distributions on Manifolds with Free-Form Flows
Our method overcomes this limitation by sampling in a single function evaluation. The key innovation is to optimize a neural network via maximum likelihood on the manifold, possible by adapting the free-form flow framework to Riemannian manifolds.
Evaluate Large Language Model as Critic
Critique ability, i.e., the capability of Large Language Models (LLMs) to identify