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…
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
Inverse problems arise in a multitude of applications, where the goal is to recover a clean signal from noisy and possibly (non)linear observations. The difficulty of a reconstruct…
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…
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
Zalan Fabian, Berk Tinaz, Mahdi Soltanolkotabi
Diffusion models have established new state of the art in a multitude of computer vision tasks, including image restoration. Diffusion-based inverse problem solvers generate recons…