Measure Neural Mappings
arXiv:2610.17384v1 Announce Type: new Abstract: We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operator
Knowledge catalogue
arXiv:2610.17384v1 Announce Type: new Abstract: We introduce and define measure neural mappings (MNMs), which combine measure-dependent neural transport with ensemble consistency for learning operator
arXiv:2512.21326v3 Announce Type: replace Abstract: Applying well-established statistical methods effectively to LLM evals requires consideration of model correlations and prediction noises. Leading e
arXiv:2610.17053v1 Announce Type: cross Abstract: Shapley-based measures are widely used for measuring variable importance in statistics and machine learning. However, these measures have a serious is
arXiv:2610.17690v1 Announce Type: cross Abstract: Medical multimodal reasoning models are typically trained on teacher-generated rationales, discarding the valid reasoning in students' own failed traj
arXiv:2610.15638v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) predict links on unseen graphs zero-shot. KGFMs have been pioneered by ULTRA which builds a relation grap
arXiv:2509.22707v2 Announce Type: replace-cross Abstract: Dynamic Voltage and Frequency Scaling is essential for enhancing energy efficiency in mobile platforms. However, traditional heuristic-based g
arXiv:2610.14976v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have excelled in joint visual and language understanding, particularly in image captioning. However, LVLMs remain p
arXiv:2610.18851v1 Announce Type: cross Abstract: Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple
arXiv:2610.17354v1 Announce Type: new Abstract: Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which
arXiv:2610.15892v1 Announce Type: cross Abstract: We study eigenpair estimation for the tensorized, density-weighted Laplace--Beltrami operator from n independent data points sampled from a multi-mani
arXiv:2610.18623v1 Announce Type: cross Abstract: We study the role of design smoothness in the minimax estimation of conditional average treatment effects at a fixed covariate point. For design densi
arXiv:2610.14907v1 Announce Type: cross Abstract: Optimal transport (OT) enables multivariate conformal prediction (CP) to construct flexible prediction sets that reflect the underlying complex distri
arXiv:2610.17244v1 Announce Type: new Abstract: Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling. Chemical building blocks reduce graph size, but make
arXiv:2610.17715v1 Announce Type: new Abstract: Flow matching has emerged as a powerful alternative to diffusion models for generative modeling, but standard conditional flow matching is fundamentally
arXiv:2610.17382v1 Announce Type: new Abstract: Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft adver
arXiv:2610.17562v1 Announce Type: new Abstract: Multi-robot exploration under communication and mobility constraints requires balancing information gain with reachability. We present a distributed sem
arXiv:2510.17063v2 Announce Type: replace-cross Abstract: Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measur
arXiv:2610.15073v1 Announce Type: new Abstract: Physical flexibility plays a significant role not only in human motions but also in robot motions. For example, a humanoid robot with passive and flexib
arXiv:2610.17178v1 Announce Type: new Abstract: Floor plan datasets underpin computational analysis of building layouts, spatial connectivity and graph machine learning. However, publicly available da
arXiv:2610.16285v1 Announce Type: cross Abstract: We propose an anomaly detection method for high-dimensional mixed-type data that maps heterogeneous features to a common scale via standardized moment
arXiv:2610.17603v1 Announce Type: new Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly superv
arXiv:2610.16212v1 Announce Type: new Abstract: Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared
arXiv:2610.13966v1 Announce Type: new Abstract: Predicting cellular responses to chemical and genetic perturbations is a central test for AI virtual cells, yet many single-cell models reduce each cond
arXiv:2610.16199v1 Announce Type: cross Abstract: Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity. However, caster misalignment du
arXiv:2610.14716v1 Announce Type: new Abstract: Random walk (RW)-based algorithms are widely used in decentralized learning due to their low communication overhead and scalability. While most existing
arXiv:2202.04785v2 Announce Type: replace-cross Abstract: We present a method for multiclass thresholding of histograms whose modes may overlap so strongly that they merge into a unimodal distribution
arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth
arXiv:2610.15215v1 Announce Type: new Abstract: Nearest-neighbor (NN) search underlies countless robotics algorithms, but many state spaces are not Euclidean: orientations live on SO(2) or SO(3) and p
arXiv:2502.13034v4 Announce Type: replace-cross Abstract: In recent years, a substantial body of work in visually grounded natural language processing has focused on real-life multimodal scenarios suc
arXiv:2610.17075v1 Announce Type: new Abstract: We propose a nonparametric approach that fuses deterministic initial forecasts and a partial realization into a probabilistic forecast using the framewo
arXiv:2603.06760v2 Announce Type: replace Abstract: Control co-design jointly optimizes a robot's physical design and its controller. Existing techniques are limited in the number of model parameters
arXiv:2610.17154v1 Announce Type: cross Abstract: Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design o
arXiv:2610.16750v1 Announce Type: new Abstract: We present NFMap, a dimensionality reduction method for 2D visualisation that extends Generative Topographic Mapping (GTM) with a bijective normalizing
arXiv:2610.18379v1 Announce Type: new Abstract: With the rapid development of deep learning, supervised convolutional neural network methods have greatly improved the performance of low-dose computed
arXiv:2610.15223v1 Announce Type: cross Abstract: Recent observations that deep learning models train at the edge of stability have renewed interest in understanding stability properties of classical
arXiv:2610.16417v1 Announce Type: new Abstract: Remote identity verification assumes that a camera stream shows a live person, yet public real-time face-swap tools can replace that person before the s
arXiv:2610.18412v1 Announce Type: new Abstract: Prompt-based adaptation offers several ways to specialize a frozen language model, ranging from natural-language prompt optimization to learned continuo
arXiv:2610.16452v1 Announce Type: new Abstract: Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues a
arXiv:2508.10688v3 Announce Type: replace Abstract: Single-image novel view synthesis requires viewpoint-dependent structural changes while retaining source-scene information and inferring unobserved
arXiv:2610.15518v1 Announce Type: new Abstract: Probabilistic ocean modeling is essential for risk-aware marine operations, climate analysis, and scientific decision-making, where downstream decisions
arXiv:2610.16586v1 Announce Type: new Abstract: Generating accurate 3D molecules requires jointly determining a variable number of atoms, their identities, and geometry across spatial scales: global s
arXiv:2610.18714v1 Announce Type: cross Abstract: Non-clashing teaching minimizes teaching complexity among models satisfying the Goldman--Mathias collusion-avoidance criterion. While the Vapnik--Cher
arXiv:2607.11432v2 Announce Type: replace Abstract: We study reinforcement learning (RL) from pairwise human-generated trajectory comparisons. We generalize preference-based RL by formalizing a novel
arXiv:2610.14131v1 Announce Type: cross Abstract: In the machine learning community, the reinforcement-learning-driven reasoning process has been intensively studied since around 2025. In this article
arXiv:2610.17451v1 Announce Type: new Abstract: Self-supervised polysomnography foundation models (PSG FMs) aim to capture clinically useful information beyond sleep staging, yet are rarely compared w
arXiv:2610.16815v1 Announce Type: cross Abstract: Calibration has been extensively studied for classifiers, but remains largely unexplored for crowdsourcing aggregation methods, which are increasingly
arXiv:2610.18333v1 Announce Type: cross Abstract: Random features provide a standard approach to scaling kernel methods to large datasets by approximating the kernel with a finite-dimensional feature
arXiv:2610.15078v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) accelerate molecular dynamics (MD) simulations by learning the energies and forces that govern atomic mot
arXiv:2610.14074v1 Announce Type: new Abstract: Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interact
arXiv:2610.16179v1 Announce Type: new Abstract: Latte art is a complex task requiring dexterity and precision, making it a challenging and commercially relevant task. A one-shot learning system is emp
arXiv:2610.16219v1 Announce Type: cross Abstract: Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existi
arXiv:2610.16464v1 Announce Type: cross Abstract: We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially
arXiv:2607.17614v2 Announce Type: replace-cross Abstract: Recent advances in deep-learning-based nonlinear system identification have led to encoder-based estimation of neural state-space (ANN-SS) mod
arXiv:2610.18210v1 Announce Type: new Abstract: This chapter provides a mathematical introduction to operator surrogates, which are finite-parametric maps approximating complex (typically nonlinear) o
arXiv:2610.16560v1 Announce Type: new Abstract: PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in term
arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl
arXiv:2509.07552v3 Announce Type: replace Abstract: We present a feed-forward framework for static Gaussian full-head reconstruction and novel-view rendering from a single unposed image. Unlike previo
arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc
arXiv:2610.14012v1 Announce Type: new Abstract: Many systems read the latent space of a variational autoencoder (VAE) as discrete codes, and rely on each latent being read back as the code it was writ
arXiv:2610.17848v1 Announce Type: new Abstract: Persistence diagrams (PDs) represent multiscale topological information as multisets of birth-death pairs (b,d). How best to use them with Transformers,