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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
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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ASEE 20232023The majority of students who choose to major in engineering do so to become a part of the community of practice of professional engineers (Johri & Olds, 2011), meaning that they want to have adequate exposure to what a career as a professional engineer could potentially be as part of their college experience. However, according to Jonassen (2014), engineering graduates are not well trained to contribute
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UAI 20232023We study the problem of best-arm identification (BAI) in the fixed-budget setting with heterogeneous reward variances. We propose two variance-adaptive BAI algorithms for this setting: SHVar for known reward variances and SHAdaVar for unknown reward variances. The key idea in our algorithms is to adaptively allocate more budget to arms with higher reward variances. The main algorithmic novelty is in the
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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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Gretchen Ertl
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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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