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August 26, 2025With a novel parallel-computing architecture, a CAD-to-USD pipeline, and the use of OpenUSD as ground truth, a new simulator can explore hundreds of sensor configurations in the time it takes to test just a few physical setups.
Featured news
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2024Large models training is plagued by the intense compute cost and limited hardware memory. A practical solution is low-precision representation but is troubled by loss in numerical accuracy and unstable training rendering the model less useful. We argue that low-precision floating points can perform well provided the error is properly compensated at the critical locations in the training process. We propose
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ACL 2024 Workshop on NLP for Conversational AI2024Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated LLM chatbots, dialogue managers, response rankers, and recommenders. The problem is worse for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent
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E-commerce stores typically test changes to ranking algorithms through rigorous A/B testing which requires a change to satisfy some predefined success criteria on multiple metrics. This problem of simultaneously optimization of multiple metrics is multi-objective-optimization (MOO). A common method for MOO is to choose a set of weights to scalarize the multiple metrics into one ranking objective. However
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Winter Simulation Conference 20242024Organizations today are integrating technologies such as cloud computing, and digital twin in their manufacturing and logistical processes. In a capital-intensive logistics industry, Discrete event simulation (DES) plays a crucial role in distribution center design, automation system performance analysis, optimization and operational planning. Developing and deploying DES models demands proficiency in various
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State-of-the-art performance has been achieved in recent years on tasks such as search, recommendation and classification using Visuo-Lingual Multi-Modal models. While the pre-trained Vision-Language models like Contrastive Language-Image Pre-training (CLIP) have achieved promising zero-shot performance on several generalized tasks by learning vision-language concepts in a common space, the natural hierarchical
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