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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ACL-IJCNLP 20212021With the ever-increasing complexity of neural language models, practitioners have turned to methods for understanding the predictions of these models. One of the most well-adopted approaches for model interpretability is feature-based interpretability, i.e., ranking the features in terms of their impact on model predictions. Several prior studies have focused on assessing the fidelity of feature-based interpretability
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ECIR 20212021A key application of conversational search is refining a user’s search intent by asking a series of clarification questions, aiming to improve the relevance of search results. Training and evaluating such conversational systems currently requires human participation, making it unfeasible to examine a wide range of user behaviors. To support robust training/evaluation of such systems, we propose a simulation
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ACL-IJCNLP 20212021Incorporating external knowledge into Named Entity Recognition (NER) systems has been widely studied in generic domain. In this paper, we focus on clinical domain where only limited data is accessible and interpretability is important. With recent advancement in technology and increased number of clinical trials has resulted in discovery of new drugs , procedures as well as medical conditions. These factors
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ACL Findings 20212021Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer-grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly
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NAACL 20212021This paper presents a production Semi-Supervised Learning (SSL) pipeline based on the student-teacher framework, which leverages millions of unlabeled examples to improve Natural Language Understanding (NLU) tasks. We investigate two questions related to the use of unlabeled data in the production SSL context: 1) how to select samples from a huge unlabeled data pool that are beneficial for SSL training,
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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.
