arXiv:2510.17901v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative decentralized training across multiple parties (nodes) while keeping raw data private. There are two main paradigms in FL: Horizontal FL (HFL), where all participant nodes share the same feature space…
arXiv:2502.01546v2 Announce Type: replace-cross Abstract: Non-prehensile pushing to move and reorient objects to a goal is a versatile loco-manipulation skill. In the real world, the object’s physical properties and friction with the floor contain significant uncertainties, which makes the task…
arXiv:2510.17914v1 Announce Type: new Abstract: We introduce NeuCo-Bench, a novel benchmark framework for evaluating (lossy) neural compression and representation learning in the context of Earth Observation (EO). Our approach builds on fixed-size embeddings that act as compact, task-agnostic representations applicable…
arXiv:2508.11503v2 Announce Type: replace-cross Abstract: Reliable autonomous navigation across the unstructured terrains of distant planetary surfaces is a critical enabler for future space exploration. However, the deployment of learning-based controllers is hindered by the inherent sim-to-real gap, particularly for the…
arXiv:2510.17915v1 Announce Type: new Abstract: Despite extensive research on neural network calibration, existing methods typically apply global transformations that treat all predictions uniformly, overlooking the heterogeneous reliability of individual predictions. Furthermore, the relationship between improved calibration and effective uncertainty-aware decision-making…
arXiv:2510.18637v1 Announce Type: cross Abstract: Semantic segmentation of electron microscopy (EM) images of biological samples remains a challenge in the life sciences. EM data captures details of biological structures, sometimes with such complexity that even human observers can find it…
arXiv:2510.17917v1 Announce Type: new Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model without requiring full retraining. Unlike concept unlearning, data unlearning in diffusion models remains underexplored and often suffers from quality degradation…
arXiv:2305.08813v2 Announce Type: replace Abstract: Nonlinear activation functions are widely recognized for enhancing the expressivity of neural networks, which is the primary reason for their widespread implementation. In this work, we focus on ReLU activation and reveal a novel and…
arXiv:2510.17923v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful paradigm for advancing Large Language Models (LLMs), achieving remarkable performance in complex reasoning domains such as mathematics and code generation. However, current RL methods face a fundamental…
arXiv:2502.08227v3 Announce Type: replace Abstract: Sample selection is a prevalent approach in learning with noisy labels, aiming to identify confident samples for training. Although existing sample selection methods have achieved decent results by reducing the noise rate of the selected…
