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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ACM Transactions on Quantum Computing Journal2022Open quantum assembly language (OpenQASM 2) [1] was proposed as an imperative programming language for quantum computation based on earlier QASM dialects [2–6]. OpenQASM is one of the programming interfaces of the IBM Quantum services [7]. In the period since OpenQASM 2 was introduced, it has become something of a de facto standard, allowing a number of independent tools to inter-operate using OpenQASM
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ICML 2022, UAI 2022 Workshop on Advances in Causal Inference2022We study the problem of observational causal inference with continuous treatment. We focus on the challenge of estimating the causal response curve for infrequently-observed treatment values. We design a new algorithm based on the framework of entropy balancing which learns weights that directly maximize causal inference accuracy using end-to-end optimization. Our weights can be customized for different
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The Web Conference 20222022Complementary product recommendation aims at providing product suggestions that are often bought together to serve a joint demand. Existing work mainly focuses on modeling product relationships at a population level, but does not consider personalized preferences of different customers. In this paper, we propose a framework for personalized complementary product recommendation capable of recommending products
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ICASSP 20222022Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of length close to the source length (e.g. within ±10% in character count), while preserving quality is a challenging task. Controlling MT output length comes at a cost to translation quality
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VLDB 20222022Although product graphs (PGs) have gained increasing attention in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error
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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.
