Knowledge catalogue
arXiv:2312.15575v2 Announce Type: replace-cross Abstract: Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging. Despite its potential, the comput
arXiv:2608.00752v1 Announce Type: new Abstract: Clinical acquisition in cardiac magnetic resonance (CMR) imaging involves obtaining cross-sectional planes of the heart along the radial and longitudina
arXiv:2608.02069v1 Announce Type: cross Abstract: Developing deployable locomotion policies through conventional reinforcement learning often requires complex reward engineering and expensive training
arXiv:2608.01963v1 Announce Type: new Abstract: Ship detection in Synthetic Aperture Radar (SAR) images plays an important role for maritime situational awareness, especially with respect to different
arXiv:2608.00855v1 Announce Type: cross Abstract: Uncrewed aerial vehicle (UAV)-enabled federated learning (FL) can provide flexible, on-demand edge intelligence for large-scale IoT deployments, but o
arXiv:2608.00903v1 Announce Type: new Abstract: In animation production, paint-bucket colourisation for hand-drawn animation is a labour-intensive procedure that assigns each enclosed region in line s
arXiv:2608.01760v1 Announce Type: new Abstract: Adverse weather image restoration aims to recover clear visibility from degraded images in complex weather conditions. Existing works attempt to address
arXiv:2608.00646v1 Announce Type: new Abstract: Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp
arXiv:2608.01475v1 Announce Type: new Abstract: Neural networks that can grow or both grow and shrink during learning, referred to as growing neural networks and elastic neural networks, respectively,
arXiv:2608.01431v1 Announce Type: cross Abstract: Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer desi
arXiv:2608.01495v1 Announce Type: new Abstract: Detection transformer models, including DETR and its extensions, learn to output a set of object-level embeddings that can be simultaneously decoded int
arXiv:2608.01348v1 Announce Type: new Abstract: Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effec
arXiv:2608.02053v1 Announce Type: cross Abstract: Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine lear
arXiv:2608.00837v1 Announce Type: new Abstract: Byte Pair Encoding (BPE) is widely used for subword tokenization, but standard BPE exposes every learned merge token to the downstream model, including
arXiv:2608.00730v1 Announce Type: new Abstract: Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional
arXiv:2510.25693v3 Announce Type: replace-cross Abstract: State-space models (SSMs) are a widely used tool in time series analysis. In the complex systems that arise from real-world data, it is common
arXiv:2506.07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deploy
arXiv:2510.14546v2 Announce Type: replace Abstract: Embeddings from Visual-Language Models are increasingly utilized to represent semantics in robotic maps, offering an open-vocabulary scene understan
arXiv:2608.00232v1 Announce Type: new Abstract: Surgical augmented reality (AR) can provide contextual guidance by overlaying virtual annotations, tool cues, and procedural information onto the surgic
arXiv:2608.01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., re
arXiv:2511.11686v4 Announce Type: replace Abstract: Speech enhancement (SE) requires high-fidelity reconstruction of clean speech that preserves linguistic and paralinguistic cues while maintaining hi
arXiv:2604.05363v2 Announce Type: replace Abstract: Infrared small target detection (IRSTD) aims to separate small targets from clutter backgrounds. Extensive research is dedicated to the pixel-level
arXiv:2608.01283v1 Announce Type: new Abstract: All Transformer-based large language models compute attention via the Euclidean inner product, an architectural choice that Dong et al. (2021) proved ca
arXiv:2608.00005v1 Announce Type: new Abstract: Peer review at major venues is under unprecedented submission pressure, motivating the use of large language models (LLMs) as review assistants. Existin
arXiv:2608.00528v1 Announce Type: new Abstract: The growth of context window lengths in Large Language Models (LLMs) significantly enhances their long-context capabilities but incurs prohibitive memor
arXiv:2608.01027v1 Announce Type: new Abstract: Robot Task and Motion Planning (TAMP) algorithms enable autonomous operation by incorporating the specific functions and constraints of end-effector too
arXiv:2512.14435v2 Announce Type: replace Abstract: Message-passing algorithms have been adapted for compressive imaging by incorporating various off-the-shelf image denoisers. However, these denoiser
arXiv:2608.02052v1 Announce Type: new Abstract: Human mobility prediction models, which forecast the next location in a user's trajectory, are increasingly deployed in urban analytics, navigation, and
arXiv:2608.02267v1 Announce Type: cross Abstract: Agents that act on a compressed representation of their history face a structural risk: if the representation aliases histories with different optimal
arXiv:2608.01318v1 Announce Type: cross Abstract: Modern sensing, communication, and learning systems generate heterogeneous network signals, with local data differing in dimension, modality, and geom
arXiv:2209.03282v5 Announce Type: replace-cross Abstract: Accelerating the convergence of second-order optimization, particularly Newton-type methods, remains a pivotal challenge in algorithmic resear
arXiv:2608.02571v1 Announce Type: new Abstract: Quadruped robots are a promising platform for search and rescue missions because they can navigate cluttered indoor environments that may be restrictive
arXiv:2608.01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference. Standard causal identification
arXiv:2608.01434v1 Announce Type: new Abstract: Normally the statistical mechanics of learning treats constraints on weight distributions as restrictions that shrink the space of possible solutions. T
arXiv:2608.01714v1 Announce Type: new Abstract: Accurate crack assessment in electroluminescence (EL) images is important for photovoltaic (PV) reliability analysis, yet existing segmentation methods
arXiv:2608.01502v1 Announce Type: cross Abstract: Sequential subset search -- forward selection with floating backtracking and its descendants -- remains the quality reference in feature selection, bu
arXiv:2608.01024v1 Announce Type: cross Abstract: We study the problem of optimizing physical layouts for automated warehouses, where hundreds to thousands of robots are coordinated to transport packa
arXiv:2608.00492v1 Announce Type: cross Abstract: Predictive coding offers a powerful theory of cortical computation, but corresponding scalable algorithmic implementations for artificial intelligence
arXiv:2603.23561v4 Announce Type: replace-cross Abstract: In the realm of machine learning theory, to prevent unnatural coding schemes between teacher and learner, No-Clash Teaching Dimension was intr
arXiv:2604.03422v2 Announce Type: replace Abstract: All languages have a noun category, but its realisation varies considerably. Depending on the language, semantic and/or morphosyntactic differences
arXiv:2608.00315v1 Announce Type: cross Abstract: Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified
arXiv:2608.00113v1 Announce Type: new Abstract: In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times; however, in rally racing, drivers intentionally break tractio
arXiv:2608.01284v1 Announce Type: new Abstract: The normalized Transformer (nGPT) realizes hyperspherical representation learning by constraining model parameter vectors and activation vectors to the
arXiv:2602.03980v2 Announce Type: replace Abstract: Any language model must decide what to say in novel contexts based on information from similar contexts. But what about contexts that are not novel
arXiv:2608.02320v1 Announce Type: cross Abstract: Traversability analysis is a fundamental capability for autonomous mobile robots operating in unstructured environments. While modern machine learning
arXiv:2608.00042v1 Announce Type: new Abstract: Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrained, high-st
arXiv:2608.02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital he
arXiv:2608.02270v1 Announce Type: new Abstract: Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agricult
arXiv:2608.01733v1 Announce Type: new Abstract: Recent advances in robot learning for manipulation have increased the importance of collecting real-world demonstration data. However, existing robotic
arXiv:2608.01573v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models achieve promising performance in robotic manipulation, typically measured by success rates aggregated over pr
arXiv:2608.00986v1 Announce Type: new Abstract: Current advancements in Multimodal Anomaly Detection (MAD) are largely driven by enhancing multimodal fusion, particularly through the integration of RG
arXiv:2608.00915v1 Announce Type: new Abstract: Uplift modeling (conditional-average-treatment-effect estimation) drives personalized targeting, yet published uplift benchmarks frequently disagree on
arXiv:2608.01220v1 Announce Type: cross Abstract: In recent years, the prevalence of large-scale data-sets and the demand for sophisti-cated learning models have necessitated the development of effici
arXiv:2608.00747v1 Announce Type: new Abstract: Large language models are increasingly integrated into autonomous robotic systems for task planning and control, but this integration exposes them to pr
arXiv:2607.08918v2 Announce Type: replace Abstract: Cyber-physical power systems are vulnerable to cascading failures caused by interdependencies between power and communication infrastructures. Becau
arXiv:2606.20324v2 Announce Type: replace-cross Abstract: Virtual training environments are software-intensive systems in which reinforcement learning (RL) agents learn, adapt, and demonstrate meaning
arXiv:2607.29626v1 Announce Type: new Abstract: As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments has become increasingly i
arXiv:2607.28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across docu
arXiv:2607.29343v1 Announce Type: new Abstract: Artificial intelligence is increasingly embedded in everyday software, making its integration into mobile apps inevitable. However, AI mobile app develo
arXiv:2607.29293v1 Announce Type: new Abstract: Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates