Search: “applications”

arXiv:2606.07086v1 Announce Type: new Abstract: Deep neural networks (DNNs) excel in computer vision tasks given large annotated datasets. In real-world applications, however, labels are often corrupt

arXiv:2606.06682v1 Announce Type: new Abstract: Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Tradi

arXiv:2606.05756v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable performance across a range of applications involving graph-structured data, particularly in

arXiv:2503.23300v2 Announce Type: replace Abstract: Understanding and predicting human visuomotor coordination is crucial for applications in robotics, human-computer interaction, and assistive techno

arXiv:2606.06177v1 Announce Type: new Abstract: Speech translation (ST) is increasingly adopted in user applications, yet its evaluation largely focuses on decontextualized testbeds and holistic quali

arXiv:2602.01429v2 Announce Type: replace Abstract: This work presents a mapless navigation approach for outdoor applications. It combines the exploratory capacity of conditional variational autoencod

arXiv:2602.20651v3 Announce Type: replace Abstract: In modern applications such as ECG monitoring, neuroimaging, wearable sensing, and industrial equipment diagnostics, complex and continuously struct

arXiv:2606.02887v1 Announce Type: new Abstract: Symmetric nonnegative matrix factorization (Symmetric NMF) approximates a matrix as WW^T with nonnegative rectangular factor W. It has broad application

arXiv:2606.03347v1 Announce Type: cross Abstract: Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because

arXiv:2410.14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.

arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themse

arXiv:2606.02750v1 Announce Type: new Abstract: Representations extracted from large language models (LLMs) play an important role in many downstream applications. However, the structure of these repr

arXiv:2606.03027v1 Announce Type: new Abstract: Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art e

arXiv:2606.03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitori

arXiv:2510.11713v4 Announce Type: replace Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge

arXiv:2606.00159v1 Announce Type: cross Abstract: Deep neural network (DNN)-based object detectors are widely used for analyzing aerial and satellite imagery in applications such as environmental moni

arXiv:2606.00112v1 Announce Type: cross Abstract: Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. H

arXiv:2602.20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

arXiv:2606.01336v1 Announce Type: new Abstract: As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a

arXiv:2602.22101v3 Announce Type: replace-cross Abstract: Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing traditio

arXiv:2606.00158v1 Announce Type: cross Abstract: Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and

arXiv:2605.31049v1 Announce Type: cross Abstract: Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems

arXiv:2502.12119v4 Announce Type: replace-cross Abstract: Visual instruction tuning adapts pre-trained Multimodal Large Language Models (MLLMs) to follow human instructions for real-world applications

arXiv:2605.30014v1 Announce Type: new Abstract: Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict acc

arXiv:2605.28999v1 Announce Type: cross Abstract: LLMs are vulnerable to prompt injection attacks. However, this vulnerability has been primarily demonstrated conceptually in academic studies or throu

arXiv:2605.29249v1 Announce Type: cross Abstract: Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. I

arXiv:2605.27843v1 Announce Type: new Abstract: An important challenge in texture recognition is the limited amount of data for training frequently found in real-world applications. In computer vision

arXiv:2605.28112v1 Announce Type: cross Abstract: Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because raw data remain local. As a result, routing

arXiv:2605.28603v1 Announce Type: cross Abstract: Irregular multivariate time series forecasting is critical in many real-world applications, where time series are irregularly sampled and exhibit dyna

arXiv:2605.26290v1 Announce Type: new Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media an

arXiv:2605.26759v1 Announce Type: new Abstract: Causal discovery from time series is critical for many real-world applications, such as tracing the root causes of anomalies. Existing approaches typica

arXiv:2605.25543v1 Announce Type: new Abstract: Accurate traffic forecasting is essential for intelligent transportation systems, supporting a wide range of real-world applications. However, it remain

arXiv:2605.24631v1 Announce Type: cross Abstract: Minority sampling aims to generate low-density instances on a data manifold and is of central importance in applications such as medical diagnosis, an

arXiv:2011.10396v3 Announce Type: replace-cross Abstract: Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view c

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency productio

arXiv:2605.24958v1 Announce Type: cross Abstract: Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especia

arXiv:2605.24366v1 Announce Type: new Abstract: Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability

arXiv:2407.03535v3 Announce Type: replace Abstract: Low-light videos often exhibit spatiotemporally incoherent noise, compromising visibility and degrading performance in computer vision applications.

arXiv:2605.23219v1 Announce Type: cross Abstract: Time series forecasting plays a central role in many real-world applications and has been extensively studied. Most existing approaches rely on determ

arXiv:2605.23504v1 Announce Type: cross Abstract: Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, lab

arXiv:2605.22471v1 Announce Type: new Abstract: Transformers have become a central architecture for graph learning, but their application to graphs requires first choosing a tokenization: a graph-to-t

arXiv:2605.21461v1 Announce Type: new Abstract: Global Navigation Satellite Systems (GNSS) are widely used to provide position, velocity, and timing (PVT) information for various applications, includi

arXiv:2605.20264v1 Announce Type: new Abstract: Human-robot collaboration in construction is often challenged by limited robot-to-human communication and the need to adapt to tolerance accumulation ar

arXiv:2503.22693v2 Announce Type: replace-cross Abstract: The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly

arXiv:2406.14978v2 Announce Type: replace Abstract: Event cameras, known for their high dynamic range, absence of motion blur, and low energy usage, have recently found a wide range of applications th

arXiv:2605.21475v1 Announce Type: new Abstract: Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational