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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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NeurIPS 2020 Machine Learning for Molecules Workshop2020Protein sequence modeling typically does not use randomized data augmentation procedures during training due to the unpredictable functional changes introduced by even simple sequence modifications. However, in this paper, we empirically explore a set of simple string manipulations, when fine-tuning semi-supervised protein models. We compare to the Tasks Assessing Protein Embeddings (TAPE) baseline models
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NeurIPS 2020 Workshop on Self-Supervised Learning2020Self-supervised representation learning has seen remarkable progress in the last few years. More recently, contrastive instance learning has shown impressive results compared to its supervised learning counterparts. However, even with the ever increased interest in contrastive instance learning, it is still largely unclear why these methods work so well. In this paper, we aim to unravel some of the mysteries
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NeurIPS 2020 Workshop on Deep Reinforcement Learning2020We propose a simple class of deep reinforcement learning (RL) methods, called FactoredRL, that can leverage factored environment structures to improve the sample efficiency of existing model-based and model-free RL algorithms. In tabular and linear approximation settings, the factored Markov decision process literature has shown exponential improvements in sample efficiency by leveraging factored environment
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COVID-19 knowledge graph: Accelerating information retrieval and discovery for scientific literatureAACL-IJCNLP 2020 Workshop on Integrating Structured Knowledge and Neural Networks for NLP (KNLP)2020The coronavirus disease (COVID-19) has claimed the lives of over one million people and infected more than thirty-five million people worldwide. Several search engines have surfaced to provide researchers with additional tools to find and retrieve information from the rapidly growing corpora on COVID-19. These engines lack extraction and visualization tools necessary to retrieve and interpret complex relations
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NeurIPS 2020 Workshop on Interpretable Inductive Biases and Physically Structured Learning2020The ability to generalize to unseen data is at the core of machine learning. A traditional view of generalization refers to unseen data from the same distribution. Dynamical systems challenge the conventional wisdom of generalization in learning systems due to distribution shifts from non-stationarity and chaos. In this paper, we investigate the generalization ability of dynamical systems in the forecasting
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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.
