Knowledge catalogue
arXiv:2606.21326v1 Announce Type: cross Abstract: We present an ML-based vessel detection and localization system, trained with weak supervision from imperfect AIS labels, that achieves a 97.8% detect
arXiv:2606.23448v1 Announce Type: new Abstract: Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instance
arXiv:2606.22449v1 Announce Type: cross Abstract: Current embodied world models are primarily optimized for predictive objectives, limiting their ability to generalize under distribution shifts and re
arXiv:2606.21027v1 Announce Type: new Abstract: Single-shot fringe projection profilometry (FPP) has been actively studied for real-time measurement, dynamic object reconstruction, and motion-sensitiv
arXiv:2503.16000v2 Announce Type: replace Abstract: We present extbf{SenseExpo}, a lightweight single-robot exploration framework that integrates a compact map prediction network into a frontier-based
arXiv:2606.21716v1 Announce Type: new Abstract: The capability to encode spatially distinct magnetization patterns within soft materials enables remote control over complex deformations. This characte
arXiv:2606.20684v1 Announce Type: new Abstract: Viewport-adaptive 360-degree imaging seeks to allocate a fixed sampling budget to the region a viewer is likely to observe. Existing view-biased project
arXiv:2606.20918v1 Announce Type: new Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimizat
arXiv:2606.21099v1 Announce Type: new Abstract: Variational inference (VI) is a powerful method for principled posterior inference for scientific inverse imaging. VI learns the posterior distribution,
arXiv:2312.07865v4 Announce Type: replace Abstract: Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques
arXiv:2606.23536v1 Announce Type: new Abstract: We propose a method for estimating time-varying traffic flow patterns from sparse aggregated vehicle counts. The method partitions the study area into s
arXiv:2606.21096v1 Announce Type: new Abstract: Semi-supervised learning (SSL) on data streams is challenging due to the continuous evolution of high-volume data and the scarcity of labels. Existing m
arXiv:2606.23357v1 Announce Type: new Abstract: Structured weight-uncertainty can improve many aspects of deep learning, but it remains costly to estimate and difficult to implement. Here, we show tha
arXiv:2510.04180v2 Announce Type: replace Abstract: Deep neural networks can achieve high accuracy while relying on evidence that is hard to inspect or misaligned with the intended task. Concept Bottl
arXiv:2606.22308v1 Announce Type: cross Abstract: Lesion segmentation in breast ultrasound involves two related challenges. In images with lesions, speckle noise, low tissue contrast, and posterior ac
arXiv:2606.21828v1 Announce Type: cross Abstract: Neural operators are increasingly used to warm-start Newton solvers for nonlinear PDEs, on the premise that a low test error places the initial guess
arXiv:2606.22874v1 Announce Type: new Abstract: Long contexts have become standard in pretrained LLMs, yet they remain expensive to run: prefill compute grows quadratically with sequence length, and e
arXiv:2606.23188v1 Announce Type: new Abstract: Black-box optimization (BBO) deals with problems where objective functions lack explicit analytical forms and are expensive to evaluate. Factorization m
arXiv:2606.18697v2 Announce Type: replace Abstract: Model-based learning agents use learned world models to predict future states, plan actions, and adapt to new environments. However, the process of
arXiv:2606.20856v1 Announce Type: new Abstract: Multi-view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distanc
arXiv:2606.23186v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial videos, enabling contact-free health monitoring. Convention
arXiv:2603.07307v2 Announce Type: replace Abstract: Recent token merging techniques for Vision Transformers (ViTs) provide substantial speedups by reducing the number of tokens processed by self-atten
arXiv:2507.03318v3 Announce Type: replace Abstract: Explainable artificial intelligence approaches accelerate drug discovery by improving molecular representation learning, identifying key molecular s
arXiv:2510.10779v5 Announce Type: replace Abstract: With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and
arXiv:2606.23477v1 Announce Type: cross Abstract: Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations
Here’s a problem you probably didn’t solve in school: You’re an ambitious young plumber from Brooklyn in a world inhabited by violent human-size mushrooms called Goombas. The love of your life has bee
arXiv:2606.22124v1 Announce Type: new Abstract: Accurate recognition of anatomical structures is essential for safe and effective minimally invasive surgery (MIS), yet it remains underexplored in surg
arXiv:2606.20864v1 Announce Type: cross Abstract: Developing robust intrusion detection systems (IDS) for IoT environments requires large, labeled datasets capturing realistic traffic distributions ac
arXiv:2606.21607v1 Announce Type: new Abstract: Vision-language models such as CLIP have recently achieved strong performance on a wide range of visual understanding tasks. However, most existing mode
arXiv:2606.17055v2 Announce Type: replace Abstract: The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based
arXiv:2606.23132v1 Announce Type: new Abstract: Vision-language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to adversarial perturbations. Recent test-time ada
arXiv:2509.21606v3 Announce Type: replace Abstract: Federated continual learning (FCL) enables collaborative model training across distributed clients on sequentially arriving tasks without revisiting
arXiv:2606.22867v1 Announce Type: cross Abstract: Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its
arXiv:2606.21189v1 Announce Type: new Abstract: Non-stationary partial differential equations (PDEs) arise throughout scientific computing, where the dominant frequency content and energy distribution
Jos Benschop is climbing a ladder to get to the top of his newest machine. It’s a bit of a schlep. The contraption is the size of a double-decker bus—more than 150 tons of gleaming precision-milled al
arXiv:2606.23306v1 Announce Type: cross Abstract: We study the limits of CTC-internal scoring for N-best hypothesis selection and locate the information bottleneck separating acoustic confidence from
arXiv:2606.21585v1 Announce Type: new Abstract: We equip the space of beliefs with a cost geometry (what it costs to pass from one belief to another): optimal transport in Wasserstein space, reweighte
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The $400 million machine powering the future of chipmaking It’
arXiv:2606.23546v1 Announce Type: new Abstract: Transformer-based models underpin modern natural language processing but incur rapidly growing computational and energy costs. As training scales in bot
arXiv:2603.29245v3 Announce Type: replace Abstract: Monocular building height estimation from optical imagery is important for characterizing urban vertical structure, yet remains challenging due to t
François Chollet announced that members of the Keras team would be attending the AI Systems DevLabs event in Sunnyvale and invited people to meet them there. This post indicates a scheduled appearance
arXiv:2602.07078v2 Announce Type: replace Abstract: Reinforcement Learning (RL) for Large Language Models (LLMs) often suffers from training collapse in long-horizon tasks due to exploding gradient va
arXiv:2511.01605v2 Announce Type: replace Abstract: Toeplitz covariance estimation is a classical problem in statistical signal processing, yet the geometry of the Gaussian maximum-likelihood objectiv
arXiv:2506.23921v5 Announce Type: replace-cross Abstract: The public often attributes human-like qualities to large language models (LLMs), assuming that they 'know' certain things. In reality, LLMs e
arXiv:2606.21579v1 Announce Type: new Abstract: Procedural mistake detection is important for quality control and user assistance across many disciplines. Recent work in this field has achieved signif
arXiv:2606.22911v1 Announce Type: cross Abstract: Multi-zone HVAC control is a spatial decision problem in which indoor thermal evolution and control decisions depend not only on outdoor conditions an
arXiv:2606.19813v1 Announce Type: cross Abstract: Thermal infrared (TIR) imaging has been a popular choice for field robotics due to its robust perception capability under low light visual degradation
arXiv:2606.23362v1 Announce Type: cross Abstract: Diffusion models (DMs), despite their impressive capabilities across a wide range of generative tasks, have been shown to be vulnerable to backdoor at
arXiv:2602.01448v2 Announce Type: replace Abstract: This paper introduces a novel robotic system designed to manage severe bleeding in emergency scenarios, including unique environments like space sta
arXiv:2606.22299v1 Announce Type: new Abstract: Idling Vehicle Detection (IVD) seeks to determine, at the final frame of a video clip, whether any vehicle is idling, meaning the vehicle is stationary
arXiv:2606.20670v1 Announce Type: new Abstract: Wireless foundation models offer a path toward reusable channel state information (CSI) intelligence for sixth-generation (6G) systems. However, existin
arXiv:2606.20933v1 Announce Type: new Abstract: The choice of loss function and optimizer is an important decision, that shapes further model training. Yet automated architecture search pipelines (Aut
arXiv:2606.21514v1 Announce Type: new Abstract: Recently, Muon has gained substantial attention as an appealing alternative to Adam-like optimizers, with many works highlighting its advantages through
arXiv:2503.23332v2 Announce Type: replace Abstract: Image generation algorithms are increasingly integral to diverse aspects of human society, driven by their practical applications. However, insuffic
arXiv:2606.20774v1 Announce Type: new Abstract: Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation p
arXiv:2606.17343v2 Announce Type: replace Abstract: We propose a novel Bayesian framework for joint image reconstruction and uncertainty quantification from compressed sensing magnetic resonance imagi
arXiv:2508.20906v3 Announce Type: replace Abstract: While foundation models have revolutionized fields such as natural language processing and computer vision, their potential in graph machine learnin
arXiv:2603.06608v2 Announce Type: replace-cross Abstract: The research community lacks a middle ground between StarCraft II full game and its mini-games. The full-game's sprawling state-action space r
arXiv:2606.21185v1 Announce Type: cross Abstract: There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed. In particular, Robi
arXiv:2606.23353v1 Announce Type: cross Abstract: Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this