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  • arXiv. org e-Print archive
    arXiv is a free distribution service and an open-access archive for nearly 2 4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics Materials on this site are not peer-reviewed by arXiv
  • [1706. 03762] Attention Is All You Need - arXiv. org
    The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration The best performing models also connect the encoder and decoder through an attention mechanism We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely
  • Log in to arXiv | arXiv e-print repository
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  • Submission Overview - arXiv info
    Home Policies Submission Guidelines While submission to arXiv is free for authors, we do ask authors to carefully prepare their work according to these guidelines This will improve discoverability of the work and reduce the likelihood of delays before announcement Submissions to arXiv should be topical and refereeable scientific contributions that follow accepted standards of scholarly
  • arXiv:1706. 03762v7 [cs. CL] 2 Aug 2023
    arXiv org offers a repository for researchers to share and access academic preprints across diverse scientific fields
  • [2501. 12948] DeepSeek-R1: Incentivizing Reasoning Capability in LLMs . . .
    General reasoning represents a long-standing and formidable challenge in artificial intelligence Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-thought prompting, have achieved considerable success on foundational reasoning tasks However, this success is heavily contingent upon extensive human-annotated demonstrations, and models' capabilities are still
  • Artificial Intelligence - arXiv. org
    Artificial Intelligence Authors and titles for recent submissions Fri, 12 Jun 2026 Thu, 11 Jun 2026 Wed, 10 Jun 2026 Tue, 9 Jun 2026 Mon, 8 Jun 2026 See today's new changes
  • [1312. 6114] Auto-Encoding Variational Bayes - arXiv. org
    How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case Our
  • About arXiv - arXiv info
    Read more arXiv offers researchers a broad range of services: article submission, compilation, production, retrieval, search and discovery, web distribution for human readers, and API access for machines, together with content curation and preservation
  • [2503. 14734] GR00T N1: An Open Foundation Model for . . . - arXiv. org
    General-purpose robots need a versatile body and an intelligent mind Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy in the human world A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly handle real-world variability, and





中文字典-英文字典  2005-2009