Knowledge catalogue
arXiv:2610.06936v1 Announce Type: new Abstract: Irregular multivariate time series forecasting is a challenging yet important problem in real-world applications, where observations are often irregular
arXiv:2610.06809v1 Announce Type: new Abstract: Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free
arXiv:2610.05825v1 Announce Type: new Abstract: Classification problem in the context of highly imbalanced data is a major challenge in many real-world applications (e.g., FinTech, healthcare, etc.).
arXiv:2410.16089v3 Announce Type: replace Abstract: The cost, flexibility, and efficiency of modern UAVs make them attractive across many applications, but their proliferation has driven a rising numb
arXiv:2609.40075v1 Announce Type: new Abstract: Partial Optimal Transport (POT) extends the classical optimal transport problem by relaxing the strict mass conservation constraint, enabling its use in
arXiv:2609.39294v1 Announce Type: new Abstract: Precise numerical reasoning with Large Language Models (LLMs) is essential for expanding their applicability to complex real-world tasks. However, text-
arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360^irc scene geometry, being essential for robotics and AR/VR applications. While perspective dept
arXiv:2609.38760v1 Announce Type: new Abstract: Scalable robotic fleets have become increasingly popular for various applications such as package delivery, warehouse management, and military operation
arXiv:2609.38538v1 Announce Type: cross Abstract: Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as b
arXiv:2602.06700v2 Announce Type: replace-cross Abstract: Graph-structured data underpin a wide spectrum of modern applications, yet their multiple sensitive attributes are not isolated but deeply cou
arXiv:2510.17611v3 Announce Type: replace Abstract: Unsupervised anomaly detection (UAD) has evolved from specialized single-class models to unified multi-class models, yet existing multi-class models
arXiv:2609.31653v1 Announce Type: cross Abstract: Sentiment analysis is a common tool for converting unstructured text into quantitative signals in finance and risk management. Yet most applications r
arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complex
As threat actors increasingly use AI to accelerate and develop cyberattacks, enterprise defenders need to rely on both AI and a critical defender’s advantage: Business context that only you possess. E
arXiv:2609.33096v1 Announce Type: new Abstract: Rapid advances in humanoid robotics have motivated growing interest in the application of humanoids for healthcare and clinical tasks. However, it remai
arXiv:2609.33232v1 Announce Type: new Abstract: Continuous-time sensing and monitoring with timely and accurate decision-making are critical for many real-world applications. In healthcare monitoring
arXiv:2609.31881v1 Announce Type: new Abstract: Dynamic text-attributed graphs (DTAGs), where nodes, edges, and textual attributes evolve over time, are crucial in applications such as social networks
arXiv:2609.30887v1 Announce Type: new Abstract: Mobile GUI agents complete tasks using GUI actions like taps and swipes. These actions are broadly applicable across applications, but reaching a naviga
arXiv:2609.29363v1 Announce Type: new Abstract: The public visibility of Artificial Intelligence (AI) is growing rapidly, driven by the positive impact of its applications across diverse fields of kno
arXiv:2509.20702v3 Announce Type: replace-cross Abstract: Recent advances in large language model (LLM) embeddings have enabled powerful representations for biological data, but most applications to d
arXiv:2411.15921v3 Announce Type: replace Abstract: The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural net
arXiv:2609.23509v1 Announce Type: new Abstract: Novel view synthesis is a key task for dynamic scene reconstruction, where high rendering speed is essential for applications such as virtual reality. E
arXiv:2609.23980v1 Announce Type: cross Abstract: AI agents now report vulnerabilities faster than maintainers can review them. Reports often depend on security properties specific to the application,
arXiv:2609.24441v1 Announce Type: new Abstract: Multivariate time-series forecasting is essential to many real-world applications. Recent large vision models (LVMs) offer a promising paradigm by trans
arXiv:2609.24226v1 Announce Type: new Abstract: Instance segmentation is a fundamental computer vision task with diverse real-world applications. Recently, prompt-driven foundation models have shown p
Identity security now determines how safely artificial intelligence agents can access systems and act across enterprise workflows. As AI agents log in to applications and execute tasks, companies must
arXiv:2609.21293v1 Announce Type: new Abstract: Autonomous software generation (ASG) aims to turn human requirements into executable applications, but delivering these applications does not necessaril
arXiv:2609.19855v1 Announce Type: cross Abstract: Geographic applications need every object inside a radius that satisfies a semantic threshold, yet embedding indexes return approximate top-ranked lis
arXiv:2509.03405v2 Announce Type: replace Abstract: Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models
arXiv:2609.19871v1 Announce Type: new Abstract: Time series forecasting has seen signicant advancements with the emergence of new deep learning models. However, forecasting time series in applications
arXiv:2609.18737v1 Announce Type: new Abstract: Underwater 3D reconstruction supports applications ranging from marine ecosystem monitoring and subsea inspection to underwater archaeology, education,
arXiv:2512.09524v2 Announce Type: replace-cross Abstract: Neural decoding is fundamental to brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leverag
arXiv:2609.17995v1 Announce Type: new Abstract: Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the un
arXiv:2602.03344v2 Announce Type: replace-cross Abstract: Robustness is widely viewed as a key challenge for real-world applications. However, because current research focuses only on difficult tasks,
arXiv:2602.07273v2 Announce Type: replace Abstract: Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physi
arXiv:2609.13919v1 Announce Type: new Abstract: This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improvin
arXiv:2609.15359v1 Announce Type: new Abstract: The surge of large language model (LLM) applications on personal devices imposes massive, bursty workloads on cloud serving infrastructure. While prefil
arXiv:2609.13146v1 Announce Type: cross Abstract: Part-aware 3D asset generation enables applications such as editing, articulation, simulation, and fabrication, yet existing methods can generate visu
arXiv:2609.11295v1 Announce Type: cross Abstract: Feature-based explanations quantify features' influence on model predictions, but are primarily designed for scalar outputs. In many applications, how
arXiv:2609.07606v1 Announce Type: new Abstract: Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction u
arXiv:2503.06867v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) is important in real-world applications. Recently, Transformer-based methods have achieved strong performance by
When building consumer-facing generative AI applications, balancing high generation quality with fast response times across diverse media types, can be challenging. KDDI, a major telecommunications ca
arXiv:2609.05212v1 Announce Type: cross Abstract: Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current
arXiv:2510.11839v3 Announce Type: replace Abstract: Time series are ubiquitous in many applications that involve forecasting, classification and causal inference tasks, such as healthcare, finance, au
arXiv:2609.03457v1 Announce Type: new Abstract: Accurate cloud resource forecasting is essential for proactive resource provisioning, maintaining Quality of Service (QoS), and reducing operational cos
AI infrastructure strategy is expanding beyond compute and storage specifications as artificial intelligence reshapes who manages enterprise applications and data. Infrastructure teams are increasingl
Integrating modern generative AI capabilities into Kotlin applications shouldn't require juggling raw HTTP clients or bridging disparate Java libraries. Today, we're excited to announce the 1.0 releas
For the ninth consecutive year, Gartner® has named Google a Leader in the Gartner Magic Quadrant™ for Strategic Cloud Platform Services, positioned furthest for Completeness of Vision. We believe this
arXiv:2609.01493v1 Announce Type: cross Abstract: Black-Box Optimization (BBO) has found broad applications, but evolutionary algorithms and Bayesian optimization face efficiency challenges as real-wo
arXiv:2608.27906v2 Announce Type: replace Abstract: Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unli
arXiv:2608.29360v1 Announce Type: new Abstract: Spatiotemporal graphs underpin applications such as traffic forecasting, weather forecasting, and healthcare monitoring. Privacy regulations such as the
arXiv:2608.27574v1 Announce Type: new Abstract: Multi-label graph learning intends to capture the intrinsic complexity of real-world applications, where one sample is often related to multiple groups
arXiv:2608.27290v1 Announce Type: new Abstract: Christmas tree plantations are economically relevant, yet a largely unexplored application domain in Remote Sensing (RS). Their delineation is challengi
arXiv:2608.27233v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using inertial measurement units (IMUs) enables a wide range of applications, yet the field still lacks a unified model
arXiv:2504.07742v4 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-b
arXiv:2608.25897v1 Announce Type: new Abstract: Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights int
arXiv:2608.24258v1 Announce Type: new Abstract: AI systems are increasingly evaluated for legally accountable settings, where correct outputs must also be justifiable against an applicable legal stand
arXiv:2311.16139v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become indispensable tools for learning from graph structured data, catering to various applications such as
The internet connected billions of people and mobile devices, putting computers in every hand. Now, we’re in the middle of the next big technology shift, deploying millions of autonomous AI agents to
arXiv:2608.23153v1 Announce Type: new Abstract: In high-stakes healthcare applications, machine learning models are frequently trained on data from one patient population and deployed on another, crea