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arXiv:2409.04332v3 Announce Type: replace Abstract: Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid...
arXiv:2409.17091v3 Announce Type: replace Abstract: In the medical field, the limited availability of large-scale datasets and labor-intensive annotation processes hinder the performance of deep models....
arXiv:2409.18498v2 Announce Type: replace Abstract: Clustering plays a crucial role in computer science, facilitating data analysis and problem-solving across numerous fields. By partitioning large datasets...
arXiv:2409.20390v2 Announce Type: replace Abstract: AI-based systems such as language models have been shown to replicate and even amplify social biases reflected in their training...
arXiv:2410.19205v5 Announce Type: replace Abstract: Given a network with an ongoing epidemic, the network immunization problem seeks to identify a fixed number of nodes to...
arXiv:2410.23742v3 Announce Type: replace Abstract: Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar...
arXiv:2411.04760v3 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) are biologically-inspired deep neural networks that efficiently extract temporal information while offering promising gains in terms...
arXiv:2411.06624v4 Announce Type: replace Abstract: Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of...
arXiv:2411.11706v4 Announce Type: replace Abstract: Current vision-language models (VLMs) show exceptional abilities across diverse tasks, such as visual question answering. To enhance user experience, recent...
arXiv:2411.12070v4 Announce Type: replace Abstract: Deep learning architectures based on convolutional neural networks tend to rely on continuous, smooth features. While this characteristics provides significant...
arXiv:2411.13672v3 Announce Type: replace Abstract: In this work, we study the computability of topological graphs, which are obtained by gluing arcs and rays together at...
arXiv:2411.16537v5 Announce Type: replace Abstract: Spatial understanding is a crucial capability that enables robots to perceive their surroundings, reason about their environment, and interact with...
arXiv:2412.00364v2 Announce Type: replace Abstract: It is widely agreed that open-vocabulary-based approaches outperform classical closed-set training solutions for recognizing unseen objects in images for semantic...
arXiv:2412.10537v4 Announce Type: replace Abstract: In federated learning (FL), data providers jointly train a machine learning model without sharing their training data. This makes it...
arXiv:2412.10999v4 Announce Type: replace Abstract: As AI agents take on increasingly long-running tasks involving sophisticated planning and execution, there is a corresponding need for novel...
arXiv:2412.12427v2 Announce Type: replace Abstract: Wireless indoor localization has attracted significant research interest due to its high accuracy, low cost, lightweight design, and low power...
arXiv:2501.03544v4 Announce Type: replace Abstract: Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, these models are vulnerable...
arXiv:2501.10896v4 Announce Type: replace Abstract: Joint message and state transmission under arbitrarily varying jamming is investigated in this paper. The problem is modeled as the...
arXiv:2501.14406v4 Announce Type: replace Abstract: Pre-trained Language Models (PLMs) have demonstrated their superiority and versatility in modern Natural Language Processing (NLP), effectively adapting to various...
arXiv:2501.16534v5 Announce Type: replace Abstract: Alignment in large language models (LLMs) is used to enforce guidelines such as safety. Yet, alignment fails in the face...
arXiv:2502.00213v4 Announce Type: replace Abstract: Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite...
arXiv:2502.01160v3 Announce Type: replace Abstract: Quantitative information flow analyses (QIF) are a class of techniques for measuring the amount of confidential information leaked by a...
arXiv:2502.07274v5 Announce Type: replace Abstract: Continual learning (CL) has traditionally focused on minimizing exemplar memory, a constraint often misaligned with modern systems where GPU time,...
arXiv:2502.09683v3 Announce Type: replace Abstract: In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of...