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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)2022The world is not static: This causes real-world time series to change over time through external, and potentially disruptive, events such as macroeconomic cycles or the COVID-19 pandemic. We present an adaptive sampling strategy that selects the part of the time series history that is relevant for forecasting. We achieve this by learning a discrete distribution over relevant time steps by Bayesian optimization
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EMNLP 2022 Workshop on Ever Evolving NLP2022In this paper, we explore class-incremental learning for intent classification (IC) in a setting with limited old data available. IC is the task of mapping user utterances to their corresponding intents. Even though class incremental learning without storing the old data yields high potential of reducing human and computational resources in industry NLP model releases, to the best of our knowledge, it hasn
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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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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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Courtesy of Pai-Ling Yin
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