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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. -
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July 9, 202610 min read
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Featured news
The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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QIP 20232023We present a quantum algorithm that has rigorous runtime guarantees for several families of binary optimization problems, including Quadratic Unconstrained Binary Optimization (QUBO), Ising spin glasses (p-spin model), and k-local constraint satisfaction problems (kCSP). We show that either (a) the algorithm finds the optimal solution in time O∗(2(0.5−c)n) for an n-independent constant c, a 2 cn advantage
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AAAI 20232023Evaluating open-domain conversation models has been an open challenge due to the open-ended nature of conversations. In addition to static evaluations, recent work has started to explore a variety of per-turn and per-dialog interactive evaluation mechanisms and provide advice on the best setup. In this work, we adopt the interactive evaluation framework and further apply to multiple models with a focus
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FM 20232023Computational notebooks are widely used for machine learning (ML). However, notebooks raise new correctness concerns beyond those found in traditional programming environments. ML library APIs are easy to misuse, and the notebook execution model raises entirely new problems concerning reproducibility. It is common to use static analyses to detect bugs and enforce best practices in software applications.
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International Conference on Automatic Face and Gesture Recognition 20232023Synthesizing high-fidelity talking head videos of an arbitrary identity, lip-synced to a target speech segment, is a challenging problem. Recent GAN-based methods succeed by training a model on a large amount of videos, allowing the generator to learn a variety of audio-lip representations. However, they are unable to handle head pose changes. On the other hand, Neural Radiance Fields (NeRFs) model the
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VLDB 20232023Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networking conditions due to large communication overheads. In this paper, we propose MiCS, which Minimizes the Communication Scale to bring down communication overhead. Specifically, by decreasing the number of participants in a communication
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
