Customer-obsessed science
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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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Interspeech 20212021By implicitly recognizing a user based on his/her speech input, speaker identification enables many downstream applications, such as personalized system behavior and expedited shopping checkouts. Based on whether the speech content is constrained or not, both text-dependent (TD) and text-independent (TI) speaker recognition models may be used. We wish to combine the advantages of both types of models through
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Interspeech 20212021The success of modern deep learning systems is built on two cornerstones, massive amount of annotated training data and advanced computational infrastructure to support large-scale computation. In recent years, the model size of state-of-the-art deep learning systems has rapidly increased and sometimes reached to billions of parameters. Herein we take a close look into this phenomenon and present an empirical
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SIGIR 2021 International Workshop on Causality in Search and Recommendation2021We propose a novel method to estimate metrics for a ranking policy, based on behavioral signal data (e.g. clicks or viewing of video contents) generated by a second different policy. Building on [1], we prove the counterfactual estimator is unbiased, and discuss its low-variance property. The estimator can be used to evaluate ranking model performance offline, to validate and selection positional bias models
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Interspeech 20212021End-to-end automatic speech recognition systems map a sequence of acoustic features to text. In modern systems, text is encoded to grapheme subwords which are generated by methods designed for text processing tasks and therefore don’t model or take advantage of the statistics of the acoustic features. Here, we present a novel method for generating grapheme subwords that are derived from phoneme sequences
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Interspeech 20212021Fine-tuning transformer-based models have shown to outperform other methods for many Natural Language Understanding (NLU) tasks. Recent studies to reduce the size of transformer models have achieved reductions of > 80%, making on-device inference on powerful devices possible. However, other resource-constrained devices, like those enabling voice assistants (VAs), require much further reductions. In this
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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.
