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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ICLR 2023 Workshop on Deep Learning for Code (DL4C)2023Code understanding and generation require learning the mapping between human and programming languages. As human and programming languages are different in vocabulary, semantic, and, syntax, it is challenging for an autoregressive model to generate a sequence of tokens that is both semantically (i.e., carry the right meaning) and syntactically correct (i.e., in the right sequence order). Inspired by this
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Web Conference 2023 Workshop on Natural-Language Processing for Social Media2023Language model pre-training has led to state-of-the-art performance in text summarization. While a variety of pre-trained transformer models are available nowadays, they are mostly trained on documents. In this study we introduce self-supervised pre-training to enhance the BERT model’s semantic and structural understanding of dialog texts from social media. We also propose a semisupervised teacher-student
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ICASSP 20232023This work focuses on modelling a speaker’s accent that does not have a dedicated text-to-speech (TTS) frontend, includ-ing a grapheme-to-phoneme (G2P) module. Prior work on modelling accents assumes a phonetic transcription is avail-able for the target accent, which might not be the case for low-resource, regional accents. In our work, we propose an approach whereby we first augment the target accent data
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ECIR 20232023AI assistants are gradually becoming embedded in our lives, utilized for everyday tasks like shopping or music. In addition to the everyday utilization of AI assistants, many users engage them with playful shopping requests, gauging their ability to understand – or simply seeking amusement. However, these requests are often not being responded to in the same playful manner, causing dissatisfaction and even
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EACL 20232023This work focuses on in-context data augmenta-tion for intent detection. Having found that aug-mentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a
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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.
