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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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NeurIPS 2022 Workshop on Distribution Shifts (DistShifts)2022Multimodal image-text models have shown remarkable performance in the past few years. However, the robustness of such foundation models against distribution shifts is crucial in downstream applications. In this paper, we investigate their robustness under image and text perturbations. We first build several multimodal benchmark datasets by applying 17 image perturbation and 16 text perturbation techniques
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SLT 20222022Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network accelerator (NNA) chips, which leverage highly parallelized fixed-point arithmetic to improve runtime memory and latency. In this work, we replicate the NNA operators during the training phase, accounting for the degradation due
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The 6th edition of the International Workshop on Machine Learning Techniques for Software Quality Evolution2022Neural Language Models for code have lead to interesting applications such as code completion and bug fix generation. Another type of code related application is the identification of code quality issues such as repetitive code and unnatural code. Neural language models contain implicit knowledge about such aspects. We propose a framework to detect code quality issues using neural language models. To handle
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NeurIPS 2022 Workshop on SyntheticData4ML2022Stuttering is a speech disorder where the natural flow of speech is interrupted by blocks, repetitions or prolongations of syllables, words and phrases. The majority of existing automatic speech recognition (ASR) interfaces perform poorly on utterances with stutter, mainly due to lack of matched training data. Synthesis of speech with stutter thus presents an opportunity to improve ASR for this type of
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NeurIPS 2022 Workshop on SyntheticData4ML2022Sparsity of the data needed to learn about anomalies is often a key challenge faced when training deep supervised models for the task of Anomaly Detection (AD). Generating synthetic data by applying pre-determined transformations that conform to a set of known invariances has shown to improve performance of such deep models. In this work we present C-GATS to show that one can learn a much larger invariance
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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.
