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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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Hosted by Danielle Perszyk, cognitive scientist at Amazon, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
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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IUI 20222022For many automated classification tasks, collecting labeled data is the key barrier to training a useful supervised model. Interfaces for interactive labeling tighten the loop of labeled data collection and model development, enabling a subject-matter expert to quickly establish the feasibility of a classifier to address a problem of interest. These interactive machine learning (IML) interfaces iteratively
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MLSys 20222022Achieving high performance for compute-intensive operators in machine learning (ML) workloads is a crucial but challenging task. Many ML and system practitioners rely on vendor libraries or auto-schedulers to do the job. While the former requires large engineering efforts, the latter only supports static-shape workloads in existing works. It is difficult, if not impractical, to apply existing auto-schedulers
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AAAI 2022 Workshop on Deep Learning on Graphs: Method and Applications2022Learning effective representations of data is an important task in machine learning. Existing methods typically compute representations or embeddings in Euclidean space, which has shortcomings in representing hierarchical structures of the underlying data. Alternatively, hyperbolic geometry offers a representation scheme that is suited for robust, high-fidelity representations of tree-structured data. In
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CHIIR 20222022Providing a justification or explanation for a recommendation has been shown to improve the users’ experience with recommender systems, in particular by increasing confidence in the recommendations. However, in order to be effective in a conversational setting, the justifications have to be appropriate for the conversation so far. Previous approaches rely on a user history of reviews and ratings of related
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ICASSP 20222022Self-supervision has shown outstanding results for natural language processing, and more recently, for image recognition. Simultaneously, vision transformers and its variants have emerged as a promising and scalable alternative to convolutions on various computer vision tasks. In this paper, we are the first to question if self-supervised vision transformers (SSL-ViTs) can be adapted to two important computer
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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
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