Knowledge catalogue
arXiv:2609.33501v1 Announce Type: new Abstract: Photoplethysmography (PPG) has become an important modality for continuous cardiovascular monitoring, including atrial fibrillation (AF) detection. Howe
arXiv:2509.22161v4 Announce Type: replace Abstract: Vector quantization, which discretizes a continuous vector space into a finite set of representative vectors (a codebook), has been widely adopted i
arXiv:2609.33088v1 Announce Type: new Abstract: Traditional change detection (CD) identifies changes between bi-temporal remote sensing images, while semantic change detection (SCD) assigns predefined
arXiv:2609.32455v1 Announce Type: new Abstract: Camera pose estimation from multi-view images remains a challenge in computer vision. Traditional methods often address this problem using Structure-fro
arXiv:2609.34718v1 Announce Type: new Abstract: Reinforcement learning (RL) changes not only what language models say, but also how much they say, often increasing response length at the cost of token
arXiv:2509.23762v4 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) have recently received increasing attention in both computational neuroscience and artificial intelligence owin
arXiv:2609.33923v1 Announce Type: cross Abstract: A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error. The estimator is well-behaved
arXiv:2609.34273v1 Announce Type: cross Abstract: The projection of queries and keys are central to the attention mechanism in Transformer architectures. While they are mathematically symmetric, they
arXiv:2510.13854v2 Announce Type: replace-cross Abstract: We introduce Rule-to-Tag (R2T), a framework that turns linguistic rules into the training signal for neural sequence taggers in low-resource l
arXiv:2609.33674v1 Announce Type: new Abstract: Graph neural networks (GNNs) are often called long-range because their architecture can connect distant nodes, but this does not show whether they use d
arXiv:2609.32595v1 Announce Type: new Abstract: Safe and robust robot navigation across diverse environments requires a high-level understanding of complex scenes and the ability to carry it into stab
arXiv:2609.34032v1 Announce Type: new Abstract: A manga reader meets a character on one page and knows them on sight a hundred pages later, without ever being handed a cast list. Re-identifying comic
arXiv:2609.33420v1 Announce Type: cross Abstract: Recovering probability measures from their moments has numerous applications, esp. in connection with the method of moments in statistics and optimiza
arXiv:2609.34257v1 Announce Type: cross Abstract: Repeatedly training language models on their own generated data may create a synthetic-data feedback loop in which errors and distributional biases ar
arXiv:2609.33854v1 Announce Type: new Abstract: Driving policies require capabilities of scene understanding and future evolution prediction. To achieve this goal, current end-to-end models typically
arXiv:2609.35405v1 Announce Type: new Abstract: Pretrained 2D foundation models offer a practical alternative to dedicated 3D pretraining for brain structural magnetic resonance imaging (sMRI), but th
arXiv:2609.34904v1 Announce Type: new Abstract: Graph neural networks (GNNs) need to exploit improved message passing without surrendering control over predictions already trusted in deployment. We in
arXiv:2609.32669v1 Announce Type: cross Abstract: Attention weights depend on token counts, which change with the representation of a signal. We study refinement symmetry: splitting a representation w
arXiv:2609.32068v1 Announce Type: new Abstract: Motion retargeting transfers motion across characters with different skeletal structures while preserving semantic intent and physical plausibility. Des
arXiv:2609.32716v1 Announce Type: new Abstract: Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits locali
arXiv:2609.34587v1 Announce Type: new Abstract: Reinforcement learning is increasingly used to post-train vision-language models for image-to-code generation, such as generating SVG code from a refere
arXiv:2609.33016v1 Announce Type: new Abstract: Large Language Model (LLM) pretraining performance is jointly shaped by three components of the training triplet: the optimizer, model architecture, and
arXiv:2609.34163v1 Announce Type: new Abstract: Vision-language navigation (VLN) is typically evaluated as a one-way task, although deployed robots may need to return after reaching a goal. We study c
arXiv:2609.35612v1 Announce Type: new Abstract: Remote sensing novel view synthesis under sparse observations remains challenging due to insufficient geometric constraints and limited cross-view super
arXiv:2609.31694v1 Announce Type: new Abstract: Removing an invisible watermark and concealing the forensic evidence are distinct objectives: successfully disrupting the embedded watermark does not im
arXiv:2609.35264v1 Announce Type: new Abstract: Camera-LiDAR detectors can continue to consume unreliable features even when both sensors remain present, synchronized, and calibrated. We introduce Bou
arXiv:2609.31630v1 Announce Type: new Abstract: Replay-based continual learning almost always consolidates in a dedicated offline phase or by interleaving replayed samples with the input stream, where
arXiv:2609.33892v1 Announce Type: new Abstract: This paper proposes a residual learning-based control framework for heterogeneous vehicle platoons subject to parametric uncertainty and external distur
arXiv:2609.33895v1 Announce Type: new Abstract: In diffusion-based generation, a neural network can be trained to predict the clean data, the noise, or the velocity from a noisy input. These predictio
arXiv:2609.34942v1 Announce Type: new Abstract: The inference efficiency of Multimodal Large Language Models (MLLMs) is severely constrained by massive visual token sequences induced by high-resolutio
arXiv:2609.33412v1 Announce Type: new Abstract: Despite rapid progress in vision-language-action (VLA) models, existing reasoning paradigms still face a fundamental state-representation mismatch in op
arXiv:2609.35593v1 Announce Type: new Abstract: 3D vision transformers such as VGGT predict camera poses and scene geometry from multi-view images in a single forward pass, but their global attention
arXiv:2609.33851v1 Announce Type: new Abstract: Contextualization, the core operation of language modeling, transmits information across words to build sentence-specific word representations. Prior wo
arXiv:2609.32899v1 Announce Type: new Abstract: Hyperbolic space can embed intrinsic hierarchies in data with low distortion due to the exponential growth of volume with distance from the origin. Howe
arXiv:2609.35394v1 Announce Type: new Abstract: Video Large Language Models (Video LLMs) have achieved remarkable progress in video understanding, but their inference efficiency is constrained by the
arXiv:2609.34566v1 Announce Type: new Abstract: Predicting observed dynamics does not establish recovery of the underlying physical mechanism. Can machine learning retrace the hidden-state reasoning b
arXiv:2609.35086v1 Announce Type: new Abstract: In this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture
arXiv:2609.32984v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a promising path for overcoming the limited visual capability of current medical imaging agents. Yet applying RS
arXiv:2609.32729v1 Announce Type: cross Abstract: AdaGrad and AdaGrad-Norm are widely used adaptive methods, but their precise behavior in stochastic convex optimization remains less understood. We fi
arXiv:2609.33716v1 Announce Type: new Abstract: Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptati
arXiv:2609.35441v1 Announce Type: new Abstract: State space models (SSMs) achieve efficient sequence processing because their affine state updates are closed under composition and can therefore be eva
arXiv:2609.35623v1 Announce Type: cross Abstract: Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups a
arXiv:2512.21109v4 Announce Type: replace Abstract: MuJoCo is a powerful and efficient physics simulator widely used in robotics. One common way it is applied in practice is through Model Predictive C
arXiv:2609.35311v1 Announce Type: new Abstract: Action-conditioned video world models predict future robot interactions from multiple cameras, yet their outputs remain disparate video collections rath
arXiv:2609.34897v1 Announce Type: new Abstract: Whole slide image classification is a fundamental task in computational pathology, where patch representation quality directly affects downstream aggreg
arXiv:2609.34326v1 Announce Type: new Abstract: Large Language Model (LLM) routers commonly rely on neural query embeddings, with larger encoders expected to better capture query intent and difficulty
arXiv:2609.34527v1 Announce Type: new Abstract: We introduce the first real-time reflection-aware Gaussian SLAM system for indoor scenes. The system features a reflection-aware TSDF-Gaussian hybrid re
arXiv:2609.33011v1 Announce Type: new Abstract: We study contextual dueling bandits with general function approximation under the Bradley-Terry-Luce (BTL) preference model. A key challenge in this set
arXiv:2609.33641v1 Announce Type: cross Abstract: Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas tur
arXiv:2609.34499v1 Announce Type: new Abstract: Graph neural networks (GNNs) excel at representation learning on Knowledge Graphs (KGs), achieving stateof-the-art performance on tasks like link predic
arXiv:2609.32183v1 Announce Type: new Abstract: High-resolution plant imaging enables detailed characterization of plant morphology, but dense scientific analysis remains limited by costly pixel-level
arXiv:2609.35707v1 Announce Type: new Abstract: Recent progress in machine learning is driven by large-scale foundation models, where scaling laws and finding optimal scaling prescriptions for archite
arXiv:2609.32837v1 Announce Type: new Abstract: Ultrasound (US) acquisition depends on the operator's ability to interpret anatomy and anticipate how the view will change with probe motion. Many robot
arXiv:2609.33518v1 Announce Type: new Abstract: Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens com
arXiv:2609.32640v1 Announce Type: cross Abstract: We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning inte
arXiv:2510.21330v2 Announce Type: replace Abstract: Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling meth
arXiv:2609.33179v1 Announce Type: cross Abstract: Constrained-option scoring reads a model's probabilities for a fixed set of permitted answers, so it returns a score even when the model is about to w
arXiv:2609.33694v1 Announce Type: new Abstract: Vision-language models (VLMs) answer visual questions by combining visual information extraction with downstream problem solving. We investigate a funda
arXiv:2609.32456v1 Announce Type: new Abstract: World modeling under partial observation requires reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded obje
arXiv:2609.33780v1 Announce Type: new Abstract: Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route dive