A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Chirag Vashist, Shichong Peng, Ke Li
RS-IMLE improves few-shot image synthesis by redesigning the prior distribution to align training and inference latent codes, yielding higher quality images than existing GAN and IMLE methods.
Narges Ahmadi, Yubo Jiao, Jônatas Augusto Manzolli, et al.
This paper proposes a three-agent workflow integrating conversational data collection, structured processing, and LLM-based prediction for travel behavior, achieving 71.5% accuracy with vision-based LLMs.
Arda Sevinc, Abdurrahman Gumus
AutoReason automatically generates chain-of-thought rationales by decomposing implicit queries into explicit questions, improving multi-step reasoning accuracy in weaker LLMs on StrategyQA and HotpotQA.
Adam Zweiger, Jyothish Pari, Han Guo, et al.
SEAL enables LLMs to self-adapt by generating their own finetuning data and update directives, using reinforcement learning to optimize downstream performance.
Unknown
This survey systematically reviews in-context learning with retrieved demonstrations, covering methods, benchmarks, and open challenges.
Unknown
This paper introduces a few-shot neuro-symbolic imitation learning framework that combines symbolic planning with neural control for long-horizon tasks.
Unknown
This paper proposes a method to factorize spatio-temporal foundation models to reduce computational costs while maintaining zero/few-shot generalization performance.
Unknown
This paper demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance.
Yisheng Song, Ting Wang, Puyu Cai, et al.
A comprehensive survey of 200+ few-shot learning papers, introducing a knowledge pyramid and analyzing challenges, applications, and future directions.
Unknown
This paper introduces Mma, a multi-modal adapter for vision-language models that enhances few-shot transfer learning by integrating visual and textual features.
J. Clusmann, F. Kolbinger, H. Muti, et al.
This paper reviews the current and future applications of large language models in medicine, discussing their potential to transform clinical practice, education, and research while highlighting challenges and limitations.
K.A. Cobbina, Tianyi Zhou
First systematic study of how demo positions in prompts cause accuracy and prediction drift in LLMs, finding start-of-prompt placement yields +6 point gains.