45 citations · 135 across the 13 of their papers we have counts for
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A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models often suffer from forgetting previously learned information when trained on new data. This problem is exacerbated in federated learning (FL), where the data is…
mL-BFGS: A Momentum-based L-BFGS for Distributed Large-Scale Neural Network Optimization
Yue Niu, Zalan Fabian, Sunwoo Lee +2
Quasi-Newton methods still face significant challenges in training large-scale neural networks due to additional compute costs in the Hessian related computations and instability i…
Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory
Sara Babakniya, Zalan Fabian, Chaoyang He +2
Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learnin…
Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks
Seyed Mohammadreza Mousavi Kalan, Zalan Fabian, A. Salman Avestimehr +1
Transfer learning has emerged as a powerful technique for improving the performance of machine learning models on new domains where labeled training data may be scarce. In this app…
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian
Samet Oymak, Zalan Fabian, Mingchen Li +1
Modern neural network architectures often generalize well despite containing many more parameters than the size of the training dataset. This paper explores the generalization capa…