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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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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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The Web Conference 20222022Explainable recommendation seeks to provide not only high-quality recommendations but also intuitive explanations. Our objective is not on generating accurate recommendations per se, but on producing user-friendly explanations through recommendation captions. Importantly, the focus of existing work has been predominantly on explaining a single item recommendation. In e-commerce websites, product recommendations
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ICASSP 20222022Maximum Likelihood Estimation (MLE) is currently the most common approach to train large scale speech recognition systems. While it has significant practical advantages, MLE exhibits several drawbacks known in literature: training and inference conditions are mismatched and a proxy objective is optimized instead of word error rate. Recently, the Optimal Completion Distillation (OCD) training method was
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ICASSP 20222022Recent advances in deep learning have drastically improved performance on many Natural Language Understanding (NLU) tasks. However, the data used to train NLU models may contain private information such as addresses or phone numbers, particularly when drawn from human subjects. It is desirable that underlying models do not expose private information contained in the training data. Differentially Private
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ICASSP 20222022Training speaker-discriminative and robust speaker verification systems without speaker labels is still challenging and worthwhile to explore. In this study, we propose an effective self-supervised learning framework and a novel regularization strategy to facilitate self-supervised speaker representation learning. Different from contrastive learning-based self-supervised learning methods, the proposed self-supervised
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CHIIR 20222022Explanations describe product recommendations in a human interpretable way in order to achieve a goal, e.g. persuade users to buy. Unlike web product search, where users have access to diverse information as to why the products might be suitable for their needs, in the voice product search domain the amount of information that can be disclosed is inherently limited. Users in general evaluate a maximum of
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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
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Courtesy of Pai-Ling Yin
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