Customer-obsessed science
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August 26, 20265 min readDiscounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. -
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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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NeurIPS 20162016In this paper, we propose novel gossip algorithms for the low-rank decentralized matrix completion problem. The proposed approach is on the Riemannian Grassmann manifold that allows local matrix completion by different agents while achieving asymptotic consensus on the global low-rank factors. The resulting approach is scalable and parallelizable. Our numerical experiments show the good performance of the
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Interspeech 20162016We propose two new methods of speech detection in the context of voice-controlled far-field appliances. While conventional detection methods are designed to differentiate between speech and nonspeech, we aim at distinguishing desired speech, which we define as speech originating from the person interacting with the device, from background noise and interfering talkers. Our two proposed methods use the first
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NeurIPS 20162016We provide a unified modeling framework of sparse and low-rank decomposition to investigate the fundamental limits of communication, computation, and storage in mobile big data systems. The resulting sparse and low-rank optimization problems are highly intractable non-convex optimization problems and conventional convex relaxation approaches are inapplicable, for which we propose a smoothed Riemannian optimization
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ECCV 20162016An adaptive image sampling framework is proposed for identifying text regions in natural scene images. A small fraction of the pixels actually correspond to text regions. It is desirable to eliminate non-text regions at the early stages of text detection. First, the image is sampled row-by-row at a specific rate and each row is tested for containing text using an 1D adaptation of the Maximally Stable Extremal
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SIGIR 20162016Voice search applications are typically evaluated by comparing the predicted query to a reference human transcript, regardless of the search results returned by the query. While we find that an exact transcript match is highly indicative of user satisfaction, a transcript which does not match the reference still produces satisfactory search results a significant fraction of the time. This paper therefore
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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.
