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July 9, 202610 min readA new Rust proxy called Turnstile sits between the model backend and the agent harness to capture information lost in mere text transcripts. -
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, 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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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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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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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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