Indoor localization systems in care facilities enable optimization of staff allocation, workload management, and quality of care delivery. Traditional machine learning approaches to Bluetooth Low Energy (BLE)-based localization treat each temporal measurement as an independent observation, fundamentally limiting their performance. To address this limitation, this paper introduces Deep Attention-based Sequential Ensemble Learning (DASEL), a novel framework that reconceptualizes indoor localization as a sequential learning problem. The framework integrates frequency-based feature engineering, bidirectional GRU networks with attention mechanisms, multi-directional […]

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Mobile Fortify, the new app used by Immigration and Customs Enforcement (ICE) to use face recognition technology (FRT) to identify people during street encounters, is an affront to the rights and dignity of migrants and U.S. citizens alike. That’s why a coalition of privacy, civil liberties and civil rights organizations are demanding the Department of Homeland Security (DHS) shut down the use of Mobile Fortify, release the agency’s privacy analyses of the app, and clarify the agency’s policy […]

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arXiv:2501.00744v2 Announce Type: replace Abstract: Chest X-ray (CXR) images are among the most commonly used diagnostic imaging modalities in clinical practice. Stringent privacy constraints often limit the public dissemination of patient CXR images, contributing to the increasing use of synthetic images produced by deep generative models for data sharing and training machine learning models. Given the high-stakes downstream applications of CXR images, it is crucial to evaluate how faithfully synthetic images reflect the underlying target distribution. We propose […]

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arXiv:2602.11187v1 Announce Type: new Abstract: The rapid growth of electronics has accelerated the adoption of 2.5D integrated circuits, where effective automated chiplet placement is essential as systems scale to larger and more heterogeneous chiplet assemblies. Existing placement methods typically focus on minimizing wirelength or transforming multi-objective optimization into a single objective through weighted sum, which limits their ability to handle competing design requirements. Wirelength reduction and thermal management are inherently conflicting objectives, making prior approaches inadequate for practical […]

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This guide would not have been possible without the help and feedback from many people. Special thanks to Prof. Joan Bruna and his class at NYU, Mathematics of Deep Learning, and to Cinjon Resnick, who introduced me to DFL and helped complete this guide. Thank you to Avital Oliver, Matt Johnson, Dougal MacClaurin, David Duvenaud, and Ricky Chen for useful contributions to this guide. Thank you to Tinghao Li, Chandra Prakash Konkimalla, Manikanta Srikar Yellapragada, Shan-Conrad Wolf, Deshana […]

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arXiv:2601.21410v2 Announce Type: replace Abstract: We introduce Statsformer, a principled framework for integrating large language model (LLM)-derived knowledge into supervised statistical learning. Existing approaches are limited in adaptability and scope: they either inject LLM guidance as an unvalidated heuristic, which is sensitive to LLM hallucination, or embed semantic information within a single fixed learner. Statsformer overcomes both limitations through a guardrailed ensemble architecture. We embed LLM-derived feature priors within an ensemble of linear and nonlinear learners, adaptively calibrating […]

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arXiv:2604.14193v1 Announce Type: new Abstract: Human 3D vision involves two distinct stages: an Experience Module, where stereo depth is extracted relative to fixation, and an Inference Module, where this experience is interpreted to estimate 3D scene properties. Paradoxically, although our experience of stereo vision does not provide us with distance information, it does affect our inferences about visual scale. We propose the Inference Module exploits a natural scene statistic: near scenes produce vivid disparity gradients, while far scenes […]

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arXiv:2603.17055v1 Announce Type: new Abstract: Image Restoration (IR) agents, leveraging multimodal large language models to perceive degradation and invoke restoration tools, have shown promise in automating IR tasks. However, existing IR agents typically lack an insight summarization mechanism for past interactions, which results in an exhaustive search for the optimal IR tool. To address this limitation, we propose a portrait-aware IR agent, dubbed PaAgent, which incorporates a self-evolving portrait bank for IR tools and Retrieval-Augmented Generation (RAG) to […]

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arXiv:2602.05062v1 Announce Type: new Abstract: Dense retrieval, which encodes queries and documents into a single dense vector, has become the dominant neural retrieval approach due to its simplicity and compatibility with fast approximate nearest neighbor algorithms. As the tasks dense retrieval performs grow in complexity, the fundamental limitations of the underlying data structure and similarity metric — namely vectors and inner-products — become more apparent. Prior recent work has shown theoretical limitations inherent to single vectors and inner-products […]

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