A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Samar Khanna, Siddhant Kharbanda, Shufan Li, et al.
Mercury introduces diffusion-based LLMs for coding, achieving up to 10x throughput over speed-optimized models while maintaining comparable quality.
Shigeru Kondo, Takashi Miura
This review explains the reaction-diffusion model as a framework for biological pattern formation, highlighting its relevance and applications in developmental biology.
Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
Generative models accelerate high-dimensional system simulations by learning effective dynamics on a lower-dimensional manifold and using diffusion models for reconstruction.
Shen Nie, Fengqi Zhu, Zebin You, et al.
LLaDA is a diffusion model trained from scratch for language modeling that matches autoregressive LLMs like LLaMA3 8B across benchmarks and solves the reversal curse.
Weigao Sun, Jiaxi Hu, Yucheng Zhou, et al.
A systematic survey of efficient LLM architectures addressing transformer limitations, covering linear/sparse models, efficient attention, MoE, hybrids, and diffusion LLMs.
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U-ViT proposes a Vision Transformer (ViT) architecture for image generation using diffusion models, treating all inputs as tokens.
Chitwan Saharia, Jonathan Ho, William Chan, et al.
SR3 adapts denoising diffusion probabilistic models to image super-resolution via iterative refinement, achieving near-perfect fool rates on face super-resolution.
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, et al.
A comprehensive survey of denoising diffusion models in computer vision, covering theoretical frameworks, relations to other generative models, and future research directions.