Knowledge catalogue
arXiv:2610.16645v1 Announce Type: new Abstract: Recovering divergence-free velocity fields from sparse and corrupted observations requires reconciling physical consistency, expressive representations,
arXiv:2610.14246v1 Announce Type: new Abstract: Variance-based methods recover a Gaussian linear structural equation model (SEM) with equal or suitably constrained error variances, either by recursive
arXiv:2610.16884v1 Announce Type: new Abstract: Continuous symmetry discovery is typically formulated as a separate estimation problem for each function or dataset. We instead investigate whether the
arXiv:2603.01786v2 Announce Type: replace Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we
arXiv:2610.16299v1 Announce Type: new Abstract: Video anomaly understanding (VAU) aims to generate language-level interpretations of anomalous events in long videos, requiring visual evidence that sup
arXiv:2610.15612v1 Announce Type: new Abstract: Accurate lesion segmentation in mammography is important for computer-aided breast cancer diagnosis, but dense pixel-level annotations remain costly and
arXiv:2603.03172v2 Announce Type: replace Abstract: Certified machine unlearning aims to provably remove the influence of a deletion set U from a model trained on a dataset S, by producing an unlearne
arXiv:2610.14889v1 Announce Type: new Abstract: Bilateral teleoperation with force feedback is becoming increasingly important, and the quality of external-torque estimation is critical to this proces
arXiv:2610.15171v1 Announce Type: new Abstract: Canonicalization resolves the rotational and permutation ambiguity of 3D point clouds by selecting a unique orbit representative, maintaining their geom
arXiv:2610.17232v1 Announce Type: new Abstract: Navigating humanoid robots through dynamic, non-convex environments requires tightly coupling onboard perception with footstep planning and dynamic loco
arXiv:2610.18166v1 Announce Type: cross Abstract: Randomly initialised neural networks of width m converge to Gaussian processes as m o infty, with two types of quantitative bounds characterising this
arXiv:2610.18482v1 Announce Type: cross Abstract: Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependen
arXiv:2610.16030v1 Announce Type: cross Abstract: Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theo
arXiv:2610.18560v1 Announce Type: cross Abstract: We prove that the last iterate of Optimistic Follow-The-Regularized-Leader (OFTRL) converges at a linear rate to a Nash equilibrium in multiplayer har
arXiv:2610.18195v1 Announce Type: cross Abstract: For sensing-aided communications, single-endpoint sensing offers only a partial view of propagation, motivating link-centric sensing across both endpo
arXiv:2610.15379v1 Announce Type: cross Abstract: Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose
arXiv:2610.18604v1 Announce Type: new Abstract: 3D part assembly aims to reconstruct a complete shape from a set of unassembled parts by predicting a rigid SE(3) transformation for each part. Because
arXiv:2610.17099v1 Announce Type: cross Abstract: Local learning reduces cross-layer training dependencies, but an efficient accelerator must still retain operands, accumulate gradients, and select qu
arXiv:2606.15896v2 Announce Type: replace-cross Abstract: Learning-based quadrupedal locomotion typically relies on complex reward formulations that entangle task specification, operational limits, ga
arXiv:2610.18174v1 Announce Type: new Abstract: Looped reasoning with dynamic depth can be viewed as relaxation toward solution attractors in a learned dynamical landscape. In practice, however, laten
arXiv:2508.11624v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a widely adopted technique in text-to-image diffusion models, enabling the personalisation of visual concepts
arXiv:2610.16722v1 Announce Type: cross Abstract: Visual contrastive decoding corrects answers using the prediction difference between the original image and a reference condition, yet the same contra
arXiv:2610.17132v1 Announce Type: new Abstract: Attention-based multiple instance learning (MIL) pools a variable-size bag of instance embeddings into one vector. In batched training, bags are padded
arXiv:2610.17109v1 Announce Type: new Abstract: Because household travel surveys combine household characteristics, variable-length person rosters, vehicle rosters, and trip chains, existing synthesis
arXiv:2610.16009v1 Announce Type: new Abstract: Not every moment in a video is equally useful for recognizing an action. A few decisive moments often carry the evidence: the instant an object moves, t
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: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