Knowledge catalogue
arXiv:2602.08515v3 Announce Type: replace-cross Abstract: This work investigates shallow physics-informed neural networks (PINNs) for solving forward and inverse problems governed by nonlinear partial
arXiv:2608.23863v1 Announce Type: new Abstract: Robots are beginning to act on world-model predictions, yet reliability is still expressed through instantaneous, model-internal signals. DreamLedger in
arXiv:2608.23723v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual fe
arXiv:2608.24105v1 Announce Type: new Abstract: Purpose: Automatic radiology report generation (RRG) has been widely explored to improve reporting accuracy and reduce radiologists' workload. Most exis
arXiv:2608.24793v1 Announce Type: new Abstract: Automated malaria diagnosis from stained blood-smear microscopy is dominated by deep convolutional neural networks that are accurate but computationally
arXiv:2608.23578v1 Announce Type: cross Abstract: Trajectory-tracking metrics such as root-mean-square error (RMSE), overshoot, and settling time are widely used to evaluate control performance in mec
arXiv:2608.24771v1 Announce Type: cross Abstract: Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early dia
arXiv:2608.24386v1 Announce Type: cross Abstract: Tensor-valued prediction is fundamental to geometric deep learning, yet uncertainty quantification (UQ) for such outputs remains an open challenge. Wh
arXiv:2608.24436v1 Announce Type: cross Abstract: Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out togeth
arXiv:2608.23877v1 Announce Type: new Abstract: We prove a depth hierarchy for ReLU neural networks in which every additional ReLU layer can save exponentially many neurons. For every ellgeq 3, a glob
arXiv:2608.24281v1 Announce Type: new Abstract: Reducing annotation requirements remains a key challenge in developing robust medical object detectors. To address this, Vision-Language (VL) object det
arXiv:2608.24109v1 Announce Type: cross Abstract: Multi-view reconstruction extends beyond surface recovery to editable and relightable mesh assets. Such assets require well-formed topology, valid UV
arXiv:2608.24724v1 Announce Type: new Abstract: Harbor seals track hydrodynamic trails with their vibrissae, enabling passive perception of moving targets in dark or turbid water. Inspired by this cap
arXiv:2608.24572v1 Announce Type: new Abstract: Capturing hand pose during dexterous manipulation remains difficult: vision-based methods degrade under occlusion and challenging lighting, while sensor
arXiv:2608.23849v1 Announce Type: new Abstract: Negative sampling determines whether a knowledge graph embedding (KGE) model learns from informative counterexamples or wastes updates on implausible co
arXiv:2608.24547v1 Announce Type: new Abstract: Simulation is central to modern engineering and science, but the cost of numerical solvers for partial differential equations (PDEs) remains a bottlenec
arXiv:2608.24877v1 Announce Type: new Abstract: Smart glasses are evolving from capture and display accessories into first-person intelligence platforms that connect human perception, persistent conte
arXiv:2608.23632v1 Announce Type: new Abstract: Process supervision has improved mathematical reasoning, where intermediate steps are naturally expressed as chains of thought. In code generation, howe
arXiv:2201.13427v2 Announce Type: replace Abstract: This article discusses a particular case of the data clustering problem, where it is necessary to find groups of adjacent text segments of the appro
arXiv:2608.24680v1 Announce Type: new Abstract: Video games provide a scalable source of training data for video world models, offering diverse environments, complex interactions, and abundant in-the-
arXiv:2608.23782v1 Announce Type: new Abstract: Continual learning faces the persistent challenge of catastrophic forgetting, where sequential task updates degrade previously acquired knowledge. While
arXiv:2608.23869v1 Announce Type: new Abstract: While state-of-the-art generative models produce high-fidelity 3D meshes, these outputs lack the physical properties required for interactive simulation
arXiv:2608.23938v1 Announce Type: cross Abstract: Modern score-based generative models have achieved remarkable empirical success in high-dimensional tasks such as image, audio, and video synthesis. T
arXiv:2503.17894v3 Announce Type: replace-cross Abstract: We propose generative learner for estimating heterogeneous treatment effects and characterizing the full distribution of causal effects. The l
arXiv:2608.23970v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in understanding and interpreting mul- timedia content. However, their ability t
arXiv:2608.24580v1 Announce Type: new Abstract: Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable co
arXiv:2608.24759v1 Announce Type: new Abstract: Multi-teacher distillation has emerged as a way to combine complementary teacher models into a single student model that exhibits the strengths of all i
arXiv:2608.24782v1 Announce Type: new Abstract: Traditional image similarity metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the Structural Similarity Index Measure (S
arXiv:2608.23855v1 Announce Type: new Abstract: We propose ICI-Time, a novel framework that reframes time series forecasting as a visual inpainting task, leveraging the generalisation power of large v
arXiv:2608.23574v1 Announce Type: cross Abstract: Each WSI slide contains thousands of candidate tissue patches, while supervision is usually available only at slide level. Existing bag-construction s
arXiv:2608.24723v1 Announce Type: new Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent rep
arXiv:2608.24518v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-
arXiv:2608.24020v1 Announce Type: cross Abstract: Generating executable parametric CAD code from dimension-annotated orthographic drawings is a challenging task requiring geometric understanding, proc
arXiv:2507.18405v3 Announce Type: replace Abstract: Vision Transformers (ViTs) face two limitations: the rigid resolution dependency of positional embeddings, which complicates cross-resolution fine-t
arXiv:2608.24093v1 Announce Type: new Abstract: Self-supervised representation learning for 4D point cloud videos is challenging because annotations are costly and reconstruction-based pretraining can
arXiv:2608.24293v1 Announce Type: new Abstract: Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with variat
arXiv:2608.24544v1 Announce Type: new Abstract: Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimatio
arXiv:2608.23814v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate strong capabilities in automated essay scoring (AES), but contemporary approaches typically employ fixed prom
arXiv:2608.24855v1 Announce Type: new Abstract: Latent world models are inherently strong encoders that transform image pixel to latent embedding, yet existing world models still rely on online trajec
arXiv:2608.24713v1 Announce Type: new Abstract: Lifted inference algorithms enable scalable probabilistic inference even for large object domains by leveraging the indistinguishability of objects in a
arXiv:2603.25507v2 Announce Type: replace-cross Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited l
arXiv:2608.24524v1 Announce Type: cross Abstract: Feature attribution is a central tool of model interpretability, yet the software through which it is applied remains fragmented: individual tools spe
arXiv:2608.24365v1 Announce Type: new Abstract: Transformer-based object trackers are renowned for their strong performance, yet dense token processing often leads to prohibitive computational cost, l
arXiv:2608.24273v1 Announce Type: new Abstract: Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forget
arXiv:2608.23982v1 Announce Type: new Abstract: Scientific reasoning requires language models to retrieve specialized knowledge and incorporate it reliably into multi-step computation. Conditional mem
arXiv:2608.24748v1 Announce Type: cross Abstract: How can humans make sense of the rapid takeoff of artificial intelligence (AI)? We studied the sensemaking dynamics of AI through an open-ended, mixed
arXiv:2601.10034v3 Announce Type: replace-cross Abstract: Decision making often exhibits context dependence that is difficult to accommodate within a single non-invasive classical probability model. T
arXiv:2608.23936v1 Announce Type: new Abstract: We present a dynamical-systems based model for resting-state functional magnetic resonance imaging (rs-fMRI), trained on a dataset of roughly 40K rs-fMR
arXiv:2604.01024v2 Announce Type: replace Abstract: We study model-based learning of finite-window policies in tabular partially observable Markov decision processes (POMDPs). A common approach to lea
arXiv:2608.23730v1 Announce Type: new Abstract: We grade MDS-UPDRS gait severity from SMPL motion using three frozen MotionAGFormer encoders as featurizers, reaching macro-F1 0.58 on a hidden, multi-s
arXiv:2608.24763v1 Announce Type: new Abstract: Procedural video-language models must solve heterogeneous tasks from the same visual evidence, including action recognition, forecasting, and procedure
arXiv:2608.24415v1 Announce Type: new Abstract: Quantitative assessment of muscle MRI is crucial for monitoring neuromuscular disorders (NMD). This study introduces an automated radiomic phenotyping f
arXiv:2608.23745v1 Announce Type: cross Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and disease
arXiv:2608.23897v1 Announce Type: cross Abstract: When a code generating language model fabricates a Python package name, an adversary who has pre-registered that name on PyPI can convert that halluci
arXiv:2608.23882v1 Announce Type: cross Abstract: Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is diff
arXiv:2607.09869v2 Announce Type: replace Abstract: The Shapley value is a widely used concept in attribution problems, as it uniquely satisfies the axioms of linearity, consistency, equal treatment,
arXiv:2602.13554v2 Announce Type: replace-cross Abstract: This article develops a four stage observability engineering framework for interaction driven sensing. The framework integrates physical model
arXiv:2604.14211v2 Announce Type: replace-cross Abstract: This thesis is an exposition of Ollivier-Ricci Curvature of metric spaces as introduced by Yann Ollivier, which is based upon the 1-Wasserstei
arXiv:2608.24731v1 Announce Type: new Abstract: We settle the minimax-optimal alternating regret, a regret notion motivated by alternating learning dynamics in games, for both online linear optimizati
arXiv:2608.24427v1 Announce Type: new Abstract: Non-parametric (partial) identification of counterfactual queries typically relies on a fully specified causal graph. Motivated by settings with incompl