Customer-obsessed science
Research areas
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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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CODASPY 2022 International Workshop on Security and Privacy Analytics (IWSPA)2022Problem: Manual data analysis for extracting useful features in web log anomaly detection can be costly and time-consuming. Automated techniques on the other hand (e.g. Auto-Encoders and CNNs based) usually require supplemental network trainings for feature extractions. Often the systems trained on these features suffer from high False Positive Rates (FPRs) and rectifying them can negatively impact accuracies
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The Web Conference 20222022Tree-based models underpin many modern semantic search engines and recommender systems due to their sub-linear inference times. In industrial applications, these models operate at extreme scales, where every bit of performance is critical. Memory constraints at extreme scales also require that models be sparse, hence tree-based models are often back-ended by sparse matrix algebra routines. However, there
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ICASSP 20222022To improve daily customer experience, kitchen assistant becomes one of the enabled service in intelligent voice assistants, presenting personalized and relevant recipes to satisfy customer requests. Current solutions for recipe recommendation suffers from two limitations: First, user-recipe interactions are modeled in a uniform manner, which neglects the diversity of user preferences on recipe adoptions
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AISTATS 20222022This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and show that the resulting p-value approximately bounds the type I error by the chosen significance level even if the panel members are dependent. We compare our approach against the most widely used Granger causality algorithm on
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ICASSP 20222022End-to-end (E2E) spoken language understanding (SLU) systems can infer the semantics of a spoken utterance directly from an audio signal. However, training an E2E system remains a challenge, largely due to the scarcity of paired audio semantics data. In this paper, we consider an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space
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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.
