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 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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A large-scale measurement of benchmark data contamination across 17 frontier language models, quantifying how evaluation data leaks into training corpora.
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DOVE is a large-scale, multi-dimensional predictions dataset released to enable more meaningful and generalizable evaluation of large language models.
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LinearRAG proposes a linear graph-based retrieval-augmented generation method for efficient retrieval on large-scale corpora.
Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.
This paper presents the first large-scale systematic study (400,000+ GPU-hours) defining a framework for predicting RL scaling in LLMs, and proposes a best-practice recipe, ScaleRL, enabling extrapolation from small runs.
Giang Nguyen, Štefan Dlugolinský, Martin Bobák, et al.
This survey provides a comprehensive overview and comparison of Machine Learning and Deep Learning frameworks and libraries for large-scale data mining, highlighting trends and massive parallelism support in the era of Big Data.
Catalin Ionescu, Dragos Papava, Vlad Olaru, et al.
Introduces Human3.6M, a large-scale dataset of 3.6 million accurate 3D human poses with synchronized images, motion capture, and depth data, along with statistical models and evaluation baselines for 3D human sensing.
Dairu Liu, Zekun Qi, Jiayu Zeng, et al.
HumanTracker introduces a large-scale, perceptually aligned benchmark and preference-based metric for evaluating humanoid motion tracking, revealing failures that kinematic metrics miss.
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Introduces ABC, a large-scale CAD model dataset for advancing geometric deep learning research.
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RoboNet is a large-scale multi-robot learning dataset and benchmark that enables sharing of robotic data across institutions to improve generalization in robot learning.
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This paper provides a comprehensive overview of RLHF, covering its core methods, challenges, and applications in building large-scale machine learning systems.
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SpatialVLM endows vision-language models with spatial reasoning capabilities by generating and training on a large-scale spatial VQA dataset.