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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 20232023Recent work has shown that large-scale annotated datasets are essential for training state-of-the-art Question Answering (QA) models. Unfortunately, creating this data is expensive and requires a huge amount of annotation work. An alternative and cheaper source of supervision is given by feedback data collected from deployed QA systems. This data can be collected from tens of millions of user with no additional
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ICLR 2023 Tiny Papers2023Importance sampling is a valuable technique in deep learning that involves sampling useful training examples more frequently to improve learning algorithms. However, obtaining reliable sample importance estimates early on in training can be challenging, as existing importance sampling methods can be computationally expensive and slow to converge. In this work, we propose a novel sampling schemed based on
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Quantization-aware and tensor-compressed training of transformers for natural language understandingInterspeech 20232023Fine-tuned transformer models have shown superior performances in many natural language tasks. However, the large model size prohibits deploying high-performance transformer models on resource-constrained devices. This paper proposes a quantization-aware tensor-compressed training approach to reduce the model size, arithmetic operations, and ultimately runtime latency of transformer-based models. We compress
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SIGIR 2023 Workshop on eCommerce2023Over a period of years, search engines have become adept at understanding and providing relevant results for short user generated queries for monolingual search. However, the brevity of search queries can be a limitation for cross-lingual e-commerce search. Previous studies have demonstrated that discourse-level context information can improve machine translation (MT) for document translation but there
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Winter Simulation Conference 20232023Fulfillment centers in the E-commerce industry are highly complex systems that houses inventory and fulfill customer orders. One of the key processes at these centers involves translating customer demands into trucks and yard operations. Truck yards with operational issues can create delays in customer orders. In this paper, we show how a scalable cloud-based hybrid simulation model is used to improve yard
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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.
