arXiv Machine Learning
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
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arXiv Machine Learning
Aug 11
SPD Learn: A Geometric Deep Learning Python Library for Neural Decoding Through Trivialization
arXiv:2602. 22895v2 Announce Type: replace-cross Abstract: Implementations of symmetric positive definite (SPD) matrix-based neural networks for neural decoding remain fragmented across research codebases and Python packages.
arXiv AI
Aug 24
SPD Matrix Learning for Neuroimaging Analysis: Perspectives, Methods, and Challenges
This review discusses how neuroimaging data can be represented as symmetric positive-definite (SPD) matrices and analyzed using the Riemannian geometry of the SPD manifold. It surveys the evolution from modality-specific SPD representations to geometric shallow and deep learning methods, emphasizing how these approaches maintain structural constraints while integrating modern AI techniques. The paper frames SPD matrix learning as a bridge between classical geometric statistics and contemporary machine learning in neuroimaging and brain‑computer interface research.
arXiv AI
Jul 10
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
arXiv:2607. 08368v1 Announce Type: new Abstract: With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning.
arXiv Machine Learning
Jun 10
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
arXiv:2604. 24012v3 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in heterogeneous, resource-constrained environments.
Hugging Face Trending Papers
Jul 9
FedOPAL: One-Shot Federated Learning via Analytic Visual Prompt Tuning
With the widespread deployment of basic models in edge intelligence, communication bandwidth has become a core bottleneck restricting the scalability of federated learning. Although one-shot federated learning alleviates this problem by minimizing communication rounds, existing iterative fine-tuning or knowledge distillation methods still face challenges such as high server-side computational costs and hyperparameter sensitivity.
arXiv Machine Learning
Jun 2
Exploiting weight-space symmetries for approximating curvature
arXiv:2606. 00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks.