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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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AAAI 2022 DE-FACTIFY Workshop: Multi-Modal Fake News and Hate-Speech Detection2022Over the years, memes became very popular as social media services growing rapidly. Understanding meme images as humans do is very complicated because of its multi-modal nature (texts on images). In this paper, we describe our approach for classifying sentiment and emotion of memes for Memotion 2.0 challenge. Assuming correlation between three sub-tasks, we implemented and compared four different multi-task
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ICASSP 20222022Audio-visual data allows us to leverage different modalities for downstream tasks. The idea being individual streams can complement each other in the given task, thereby resulting in a model with improved performance. In this work, we present our experimental results on action recognition and video summarization tasks. The proposed modeling approach builds upon the recent advances in contrastive loss based
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ICASSP 20222022We present a general model for acoustic wave decomposition (AWD) on a rigid surface for a general microphone array configuration. The decomposition is modeled as a sparse recovery optimization problem that is independent of the shape of the rigid surface or the microphone array geometry. We describe an efficient algorithm for solving the optimization problem for broadband signals, and establish its effectiveness
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ICASSP 20222022This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weighted combination of random data point and label pairs for deep neural network training. Mixup has attracted increasing attention due to its ability to improve robustness and generalization of deep neural networks. Although mixup
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AISTATS 20222022We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predictor has the form of a threshold model, composed of a scoring function and a set of thresholds, and our strategy is based on a reduction to fair binary classification for
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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.
