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Found 9 papers, 1 papers with code
Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation
To avoid this problem, we construct object connections based on observations from all viewpoints in the navigational environment, which ensures complete spatial coverage and eliminates the gap, called Spatial Object Relations (SOR).
Communication Efficient and Provable Federated Unlearning
We start by giving a rigorous definition of \textit{exact} federated unlearning, which guarantees that the unlearned model is statistically indistinguishable from the one trained without the deleted data.
CPCL: Cross-Modal Prototypical Contrastive Learning for Weakly Supervised Text-based Person Re-Identification
Weakly supervised text-based person re-identification (TPRe-ID) seeks to retrieve images of a target person using textual descriptions, without relying on identity annotations and is more challenging and practical.
FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality
Federated learning is a powerful technique that enables collaborative learning among different clients.
Resource-Adaptive Newton's Method for Distributed Learning
Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance.
Byzantine-Resilient Federated Learning at Edge
To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously.
Collaborative Learning in General Graphs with Limited Memorization: Complexity, Learnability, and Reliability
The efficacy of our proposed three-staged collaborative learning algorithm is finally verified by extensive experiments on both synthetic and real datasets.
A Distributed Privacy-Preserving Learning Dynamics in General Social Networks
In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology.
Meta Matrix Factorization for Federated Rating Predictions
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
