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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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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ICCV 20212021Interactive retrieval for online fashion shopping provides the ability to change image retrieval results according to the user feedback. One common problem in interactive retrieval is that a specific user interaction (e.g., changing the color of a T-shirt) causes other aspects to change inadvertently (e.g., the retrieved item has a sleeve type different than the query). This is a consequence of existing
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KDD 2021 International Workshop on Industrial Recommendation Systems2021Recommending sets of items that include both personalized and compatible items is crucial to personalized styling programs such as Amazon’s Personal Shopper. There is both an extensive literature on learning generic fashion compatibility and also on personalization in fashion. However, recommending pairs of items that the customer would like to wear together is still less studied as it involves learning
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KDD 20212021Query auto-completion (QAC) is a fundamental feature in search engines where the task is to suggest plausible completions of a prefix typed in the search bar. Previous queries in the user session can provide useful context for the user’s intent and can be leveraged to suggest auto-completions that are more relevant while adhering to the user’s prefix. Such session-aware QACs can be generated by recent sequence-to-sequence
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AETS 20212021Formative assessment can be used by learning designers to evaluate a learners' comprehension, learning needs, and learning progress during a lesson, unit, or course. The general goal of a formative assessment is to collect detailed information that can be used to improve instruction and learning while learning is happening. Designing effective formative assessments for complex or technical knowledge can
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KDD 2021 Workshop on Pretraining: Algorithms, Architectures, and Applications2021In this paper, we introduce Graphire, an intent discovery system leveraging pretraining on predefined intents to automatically discover novel intents for intelligent personal assistants (IPA). In order to transfer the prior knowledge of predefined intents, Graphire first transforms predefined class memberships into pairwise relationships, and then learns a Siamese Neural Network (SNN) model classifying
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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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