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pro vyhledávání: '"Patil, Shishir G."'
Autor:
Stoica, Ion, Zaharia, Matei, Gonzalez, Joseph, Goldberg, Ken, Zhang, Hao, Angelopoulos, Anastasios, Patil, Shishir G., Chen, Lingjiao, Chiang, Wei-Lin, Davis, Jared Q.
Despite the significant strides made by generative AI in just a few short years, its future progress is constrained by the challenge of building modular and robust systems. This capability has been a cornerstone of past technological revolutions, whi
Externí odkaz:
http://arxiv.org/abs/2412.05299
Autor:
Patil, Shishir G., Zhang, Tianjun, Fang, Vivian, C., Noppapon, Huang, Roy, Hao, Aaron, Casado, Martin, Gonzalez, Joseph E., Popa, Raluca Ada, Stoica, Ion
Large Language Models (LLMs) are evolving beyond their classical role of providing information within dialogue systems to actively engaging with tools and performing actions on real-world applications and services. Today, humans verify the correctnes
Externí odkaz:
http://arxiv.org/abs/2404.06921
Autor:
Zhang, Tianjun, Patil, Shishir G., Jain, Naman, Shen, Sheng, Zaharia, Matei, Stoica, Ion, Gonzalez, Joseph E.
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private dom
Externí odkaz:
http://arxiv.org/abs/2403.10131
Autor:
Packer, Charles, Wooders, Sarah, Lin, Kevin, Fang, Vivian, Patil, Shishir G., Stoica, Ion, Gonzalez, Joseph E.
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propos
Externí odkaz:
http://arxiv.org/abs/2310.08560
Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls rem
Externí odkaz:
http://arxiv.org/abs/2305.15334
Cloud applications are increasingly distributing data across multiple regions and cloud providers. Unfortunately, wide-area bulk data transfers are often slow, bottlenecking applications. We demonstrate that it is possible to significantly improve in
Externí odkaz:
http://arxiv.org/abs/2210.07259
Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to relatively small models with simple architectures because training is b
Externí odkaz:
http://arxiv.org/abs/2207.07697
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This handbook provides thorough, in-depth, and well-focused developments of artificial intelligence (AI), machine learning (ML), deep learning (DL), natural language processing (NLP), cryptography, and blockchain approaches, along with their applicat