Customer-obsessed science


Research areas
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February 27, 2025Prototype is the first realization of a scalable, hardware-efficient quantum computing architecture based on bosonic quantum error correction.
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Featured news
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2025Toxicity text detectors can be vulnerable to adversarial examples - small perturbations to input text that fool the systems into wrong detection. Existing attack algorithms are time-consuming and often produce invalid or ambiguous adversarial examples, making them less useful for evaluating or improving real-world toxicity content moderators. This paper proposes an annotation pipeline for quality control
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2025This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative task that is designed to improve the quantity and diversity of the room impulse responses dataset so that it can be used for spatially sensitive downstream tasks: speaker distance estimation. The challenge identifies the technical
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ICLR 2025 Workshop on Tackling Climate Change with Machine Learning2025Accurately quantifying product carbon footprints (PCFs) is critical for organizations to measure environmental impacts and develop decarbonization strategies. However, traditional methods require Bills of Materials (BOMs) data as a key input for PCF estimation, which is time-intensive and limits scalability. We present Palimpsest, an automated BOM generation algorithm given product specification as input
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2025Jailbreaking large-language models (LLMs) involves testing their robustness against adversarial prompts and evaluating their ability to withstand prompt attacks that could elicit unauthorized or malicious responses. In this paper, we present TurboFuzzLLM, a mutation-based fuzzing technique for efficiently finding a collection of effective jailbreaking templates that, when combined with harmful questions
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CVPR 2025 Workshop on Computer Vision in Sports2025Vision Language Models (VLMs) have demonstrated strong performance in multi-modal tasks by effectively aligning visual and textual representations. However, most video understanding VLM research has been domain-agnostic, leaving the understanding of their transfer learning capability to specialized domains under-explored. In this work, we address this by exploring the adaptability of open-source VLMs to
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