Videos

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Viewing 31-40 of 261 videos
  • Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models Thumbnail

    Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models

    August 31, 2023  |  Mayee Chen, PhD Student, Stanford University
    Bio: Mayee Chen is a PhD student in the Computer Science department at Stanford University advised by Professor Christopher Ré. She is interested in understanding and improving how models learn from data. Recently, she has focused on problems in data selection, data labeling, and data representations, especially…
  • From Compression to Convection: A Latent Variable Perspective Thumbnail

    From Compression to Convection: A Latent Variable Perspective

    August 30, 2023  |  Prof. Stephan Mandt/UC Irvine
    Abstract: Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent…
  • Objective Mismatch in Reinforcement Learning from Human Feedback Thumbnail

    Objective Mismatch in Reinforcement Learning from Human Feedback

    August 29, 2023  |  Nathan Lambert
    Abstract: Reinforcement learning from human feedback (RLHF) has been shown to be a powerful framework for data-efficient fine-tuning of large machine learning models toward human preferences. RLHF is a compelling candidate for tasks where quantifying goals in a closed form expression is challenging, allowing…
  • Avenging Polanyi's Revenge: Exploiting the Approximate Omniscience of LLMs in Planning without Deluding Yourself In the Process Thumbnail

    Avenging Polanyi's Revenge: Exploiting the Approximate Omniscience of LLMs in Planning without Deluding Yourself In the Process

    August 21, 2023  |  Subbarao Kambhampati
    Abstract: LLMs are on track to reverse what seemed like an inexorable shift of AI from explicit to tacit knowledge tasks. Trained as they are on everything ever written on the web, LLMs exhibit "approximate omniscience"--they can provide answers to all sorts of queries, with nary a guarantee. This could herald a…
  • Machine Learning in Climate Action Thumbnail

    Machine Learning in Climate Action

    July 19, 2023  |  David Rolnick
    Abstract: Machine learning (ML) can be a useful tool in helping society reduce greenhouse gas emissions and adapt to a changing climate. In this talk, we will explore opportunities and challenges in ML for climate action, from optimizing electrical grids to monitoring crop yield, with an emphasis on how to…
  • Imaginative Vision Language Models Thumbnail

    Imaginative Vision Language Models

    June 26, 2023  |  Mohamed Elhoseiny, Assistant Professor/KAUST
    Bio: Mohamed Elhoseiny is an assistant professor of Computer Science at KAUST., He has become a senior member of IEEE since Fall 2021 and AAAI since Spring 2022. He is also a member of the international Summit community. Previously, he was a visiting Faculty at Stanford Computer Science department (2019-2020…
  • Do language models have coherent mental models of everyday things? Thumbnail

    Do language models have coherent mental models of everyday things?

    June 22, 2023  |  Yuling Gu
    When people think of everyday things like an egg, they typically have a mental image associated with it. This allows them to correctly judge, for example, that "the yolk surrounds the shell" is a false statement. Do language models similarly have a coherent picture of such everyday things? To investigate this, we…
  • When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories Thumbnail

    When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

    June 6, 2023  |  Alex Mallen
    Presentation of ACL 2023 main conference long paper "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories". Alex Mallen*, Akari Asai*, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi Despite their impressive performance on diverse tasks, large…
  • Structure Modeling in Language Models Thumbnail

    Structure Modeling in Language Models

    June 6, 2023  |  Yuntian Deng
    Abstract: We are approaching a future where text generation technologies will allow us to generate texts that are not only fluent at a surface level, but also coherent in their overall structure. To enable this future, my research focuses on evaluating and improving structure modeling in language models. In the…
  • Enhancing the Reliability and Continual Improvement of Neural Dialogue Systems Thumbnail

    Enhancing the Reliability and Continual Improvement of Neural Dialogue Systems

    June 5, 2023  |  Prakhar Gupta
    Neural dialogue systems have made impressive advancements in natural language understanding and generation, yet challenges in reliability and continual improvement persist. In this talk, I will present my work focused on improving the reliability of dialogue systems while emphasizing continual enhancement and…