Cooperative Sheaf Neural Networks
arXiv:2507.00647v4 Announce Type: replace Abstract: Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophi
Knowledge catalogue
arXiv:2507.00647v4 Announce Type: replace Abstract: Sheaf diffusion has recently emerged as a promising design pattern for graph representation learning due to its inherent ability to handle heterophi
arXiv:2610.04559v1 Announce Type: new Abstract: External causal reports can improve structure learning from limited observations, but their reliability varies across sources and variable pairs. We int
arXiv:2610.03911v1 Announce Type: new Abstract: Traditional sleep-staging methods apply the same model to every EEG epoch. Such uniform deployment expends computation on epochs that a smaller model co
arXiv:2512.21029v2 Announce Type: replace-cross Abstract: We study the critical points over an algebraic variety of an optimization problem defined by a quadratic objective that is degenerate. This sc
arXiv:2610.06663v1 Announce Type: new Abstract: A significant problem in endoscopic image analysis is that the machine learning (ML) models used for this purpose usually underperform when applied on i
arXiv:2610.05157v1 Announce Type: new Abstract: Gastric Adenocarcinoma is a leading cause of cancer mortality. Although 'Inflamed/Non-Inflamed' subtypes have been proposed to predict immunotherapy res
arXiv:2610.04260v1 Announce Type: new Abstract: Subliminal learning is a phenomenon where a student language model acquires a teacher model's behavioral traits by training on semantically unrelated ou
arXiv:2610.05330v1 Announce Type: cross Abstract: Time series often contain recurring structural patterns, and efficiently mining such patterns into compact representations is essential for scalable a
arXiv:2610.04938v1 Announce Type: new Abstract: We propose D-DOIT (Discrete Doob-Oriented Inference-time Transformation), a training-free and efficient adaptation method for discrete diffusion models
arXiv:2610.04783v1 Announce Type: new Abstract: Counterfactual explanations are most useful when they can be generated with low latency, remain close to the factual input, and satisfy input-domain, ca
arXiv:2601.19403v2 Announce Type: replace-cross Abstract: Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solut
arXiv:2610.06823v1 Announce Type: new Abstract: Large longitudinal cohorts often contain wrist accelerometry without optical heart-rate sensing, motivating recovery of cardiac information from motion
arXiv:2610.05632v1 Announce Type: new Abstract: Partial observation and delay-coordinate reconstruction are rooted in deterministic dynamical-systems theory, whereas there are many systems with intrin
arXiv:2510.12680v2 Announce Type: replace Abstract: Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet o
arXiv:2610.06674v1 Announce Type: cross Abstract: Nighttime lights from NASA's Black Marble product suite capture thermal and light emission signals from anomalous events including fires, volcanic eru
arXiv:2610.04159v1 Announce Type: new Abstract: Radiology report generation models can produce fluent text while still containing finding-level inaccuracies. Diagnosis-driven methods improve generatio
arXiv:2610.06636v1 Announce Type: new Abstract: Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often deg
arXiv:2610.04526v1 Announce Type: cross Abstract: Disasters can overload cellular control-plane signaling within minutes, yet fine-grained Radio Resource Control (RRC) or Next Generation (NG) Applicat
arXiv:2610.05789v1 Announce Type: cross Abstract: We investigate decentralized nonsmooth nonconvex stochastic optimization over a network of n nodes, with the goal of finding an (elta,epsilon)-Goldste
arXiv:2610.06282v1 Announce Type: new Abstract: We introduce a novel invariance for peptide tandem mass spectrometry data, unlocking self-supervised representation learning that improves de novo seque
arXiv:2610.05071v1 Announce Type: new Abstract: Learning population dynamics from unpaired temporal marginals is an ill-posed inverse problem that requires structural assumptions on the underlying dyn
arXiv:2610.04775v1 Announce Type: new Abstract: In this paper, we present a distributed cascade force control system (DCFC) for multiple robots with the aim of pushing a rigid object towards a desired
arXiv:2610.03833v1 Announce Type: cross Abstract: A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being g
arXiv:2601.23027v2 Announce Type: replace Abstract: Long chain-of-thought reasoning (Long CoT) is now fundamental to state-of-the-art LLMs, especially in mathematical reasoning. However, LLM generatio
arXiv:2610.04278v1 Announce Type: new Abstract: Deep remaining-useful-life (RUL) models on NASA C-MAPSS are now routine, and so are heatmaps that colour sensors and timesteps. A heatmap that looks mec
arXiv:2610.05529v1 Announce Type: new Abstract: We present DynaMesh, a dynamic texture generation method for 3D meshes. Given a textureless shape and a text prompt describing an effect, our method pro
arXiv:2610.06329v1 Announce Type: new Abstract: Modern LLM training increasingly relies on heterogeneous data sources spanning different domains, tasks, preference distributions, and difficulty levels
arXiv:2610.03942v1 Announce Type: new Abstract: When using diffusion models to target image restoration problems, diffusion inversion is typically employed to retain relevant image information from th
arXiv:2603.07904v3 Announce Type: replace Abstract: Vision-language-action (VLA) models achieve favorable task performance, yet runtime errors in closed-loop execution evolve with alternating updates
arXiv:2610.04554v1 Announce Type: new Abstract: Recovering detailed geometry from high-resolution images is critical for precise perception of the surroundings and objects. However, existing methods w
arXiv:2508.14086v4 Announce Type: replace Abstract: Electroencephalography (EEG) is a critical tool for monitoring brain activity and diagnosing neurological disorders such as epilepsy. However, learn
arXiv:2408.16803v2 Announce Type: replace Abstract: Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains und
arXiv:2610.05607v1 Announce Type: new Abstract: Emergency obstacle avoidance requires a mobile robot to brake or change its heading within the available distance. This paper investigates the dependenc
arXiv:2610.04726v1 Announce Type: new Abstract: Full actuation is commonly valued for independent position and attitude tracking. This work establishes a complementary principle: when only a position
arXiv:2610.05235v1 Announce Type: new Abstract: Surface electromyography (sEMG) is a low-power, cost-effective biosignal for hand-pose estimation and gesture classification. In this work, we examine w
arXiv:2610.06545v1 Announce Type: new Abstract: We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based
arXiv:2510.23087v2 Announce Type: replace Abstract: In robot-assisted minimally invasive surgery, accurate 3D reconstruction from endoscopic video is vital for downstream tasks and improved outcomes.
arXiv:2610.05996v1 Announce Type: new Abstract: Latent world models based on Joint-Embedding Predictive Architecture (JEPA) are deterministic by design. While successful in fully observable scenarios,
arXiv:2610.04066v1 Announce Type: new Abstract: Automatic calving-front delineation from synthetic aperture radar imagery is challenging because the front is a thin and often ambiguous boundary betwee
arXiv:2312.00113v4 Announce Type: replace Abstract: We present ContinuityCam, a novel approach to generate a continuous video from a single static RGB image and an event camera stream. Conventional ca
arXiv:2604.26654v1 Announce Type: cross Abstract: This paper presents an application of the biologically realistic JASTAP neural network model to classification tasks. The JASTAP neural network model
arXiv:2610.04746v1 Announce Type: cross Abstract: Simulation is a fundamental computational primitive in reinforcement learning (RL), yet conventional simulation explicitly generates individual trajec
arXiv:2610.04085v1 Announce Type: cross Abstract: Balanced discrete optimal transport between n sources and n targets of unit mass is exactly the minimum-cost assignment problem-a bipartite perfect ma
arXiv:2610.05207v1 Announce Type: new Abstract: Feed-forward 3D models provide strong multi-view geometric priors, while on- line simultaneous localization and mapping (SLAM) relies mainly on local me
arXiv:2610.05279v1 Announce Type: new Abstract: In federated learning, mitigating class imbalance is essential to improve minority-class performance. A common approach to address this problem is to au
arXiv:2610.04538v1 Announce Type: new Abstract: Reward-guided image editing at test time seeks to improve a specified reward while preserving source content and visual plausibility. Many existing appr
arXiv:2610.06810v1 Announce Type: cross Abstract: We study agnostic tomography of pure bosonic Gaussian states: given copies of an arbitrary n-mode bosonic state rho, the goal is to output a pure Gaus
arXiv:2610.05296v1 Announce Type: new Abstract: Most deep learning weather models assign a fixed set of variables and pressure levels to predefined channels, limiting transfer across atmospheric field
arXiv:2610.05880v1 Announce Type: new Abstract: Resting-state fMRI is important in the classification of brain disorders, but highly multimodal and exhibits strong multisite heterogeneity. Existing me
arXiv:2603.08433v2 Announce Type: replace Abstract: The Foret Montmorency (FoMo) dataset is a comprehensive multi-season data collection, recorded over the span of one year in a boreal forest. Featuri
arXiv:2610.03755v1 Announce Type: new Abstract: The magnitude of the generalized Euclidean cross product is a Gram volume whose degree under common scaling is fixed by the integer dimension of the spa
arXiv:2610.06575v1 Announce Type: new Abstract: FrontVeg V2 is an open-source, training-free software framework for foregroundaware zero-shot segmentation of plant traits in high-resolution images of
arXiv:2610.05801v1 Announce Type: new Abstract: We propose a Gauss-map variation (GMV) model for image denoising that measures the spatial variation of the tangent-plane projectors of the scaled image
arXiv:2610.03757v1 Announce Type: cross Abstract: Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth
arXiv:2610.05026v1 Announce Type: new Abstract: Vision-language-action (VLA) models encode semantic information from vision-language pretraining, but manipulation also requires precise spatial reasoni
arXiv:2610.05874v1 Announce Type: new Abstract: Scalable Graph Transformers are commonly trained and evaluated on static large graphs in a transductive setup. Many scalable Graph Transformer component
arXiv:2610.06042v1 Announce Type: new Abstract: Discovery of causal relationships in high-dimensional Gene Regulatory Networks (GRN) is computationally challenging and often difficult to interpret due
arXiv:2508.11880v2 Announce Type: replace Abstract: Convolutional Neural Networks (CNNs) are an effective approach for classification tasks, particularly when the training dataset is large. Although C
arXiv:2610.04568v1 Announce Type: cross Abstract: We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift
arXiv:2610.06688v1 Announce Type: new Abstract: Factories, museums and surveyors photograph the same space months apart and need to know which objects changed. When each visit is reconstructed with 3D