A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This study presents a low-rank adaptation method for finetuning large language models, embedding a semantic-aware low-rank adaptation module into attention layers.
Karsten Roth, Vishaal Udandarao, Sebastian Dziadzio, et al.
This paper introduces FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints, and provides comprehensive guidance for effective continual model updates in real-world deployment scenarios.
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This paper explores optimal training strategies for protein language models, providing guidance on compute-efficient scaling in biological research.
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This paper investigates when offline reinforcement learning outperforms behavioral cloning, providing guidance on algorithm selection based on data characteristics.
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This paper proposes a general multimodal RAG system for retail, offering practical implementation guidance for enhanced information retrieval and generation.
Priyanka Kargupta, S. Li, Haocheng Wang, et al.
This paper synthesizes cognitive science into a taxonomy of 28 cognitive elements, evaluates 192K LLM traces across modalities, and develops test-time guidance improving performance by up to 66.7%.
Feng Zhao, Xianghua Xie
This paper surveys interactive segmentation techniques for medical images, combining automatic methods with user guidance to improve accuracy and reduce manual effort.
Anil K. Jain, M. Narasimha Murty, Patrick J. Flynn
This paper provides a comprehensive overview of clustering methods from a statistical pattern recognition perspective, offering a taxonomy and practical guidance for practitioners.
Jinuk Kim, Marwa El Halabi, Wonpyo Park, et al.
GuidedQuant improves post-training quantization of large language models by using end-loss guidance to better preserve model accuracy.