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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Kaleido introduces a family of generative models for photorealistic, unified object- and scene-level neural rendering by treating 3D as a sequence-to-sequence problem.
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UniLat3D introduces geometry-appearance unified latents for single-stage 3D generation, evaluated against state-of-the-art models like Hunyuan3D2.1 and TRELLIS.
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SSDNeRF unifies 3D generation and reconstruction by conjoining a triplane NeRF auto-decoder with a single-stage diffusion model.
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Tune-A-Video enables one-shot text-to-video generation by tuning a pretrained image diffusion model on a single video.
Liangchen Li, Jiajun He
Bidirectional consistency models extend consistency models to enable both forward and reverse sampling, improving image generation quality and flexibility.
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AudioLCM introduces a latent consistency model for efficient text-to-audio generation, enabling high-quality synthesis in few sampling steps.
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This paper introduces flow matching and diffusion models, explaining and deriving flow matching as a scalable algorithm underlying large-scale generative models.
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The paper proposes a differential approach for data and classification service-based privacy-preserving machine learning in cloud environments, building on prior work in differentially private machine learning.
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AutoDEUQ automates deep ensemble construction with uncertainty quantification, evaluated on 10 regression benchmark datasets.
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The paper presents methods for efficient and effective uncertainty quantification in large language models, aiming to provide scalar estimates of answer correctness.
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This paper explores uncertainty quantification for black-box large language models in natural language generation, addressing a previously under-explored problem.
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A large-scale measurement of benchmark data contamination across 17 frontier language models, quantifying how evaluation data leaks into training corpora.