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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FreeNeRF introduces free frequency regularization to improve few-shot neural rendering by addressing frequency-related challenges in NeRF training.
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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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Tune-A-Video enables one-shot text-to-video generation by tuning a pretrained image diffusion model on a single video.
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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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Q-dit introduces a post-training quantization method for diffusion transformers that reduces memory and latency while maintaining generation quality.
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Proposes efficient privacy-preserving machine learning for blockchain networks, addressing scalability and confidentiality challenges in decentralized applications.
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MD-ML introduces a super fast privacy-preserving machine learning protocol with malicious security against a dishonest majority.
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Proposes an alternative account of AI alignment focused on the fair treatment of claims, addressing limitations of goal- or value-based approaches.
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Fortuna is an open-source library for uncertainty quantification in deep learning, addressing the gap between theory and practice.
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AutoDEUQ automates deep ensemble construction with uncertainty quantification, evaluated on 10 regression benchmark datasets.
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Proposes a fast, scalable nonparametric method to quantify uncertainty in predictions from a single deterministic neural network.
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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.