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Blavatnik School of Computer Science and AI, Department of Electrical and Computing Engineering, Tel Aviv University, Princeton University, Technion
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Local linear convergence in homogeneous deep networks can be achieved even when global convergence fails, reshaping our understanding of continual learning dynamics.
Unlock the potential of Kolmogorov-Arnold Networks with WS-KAN, a weight-space architecture that understands their hidden symmetries and predicts their performance far better than generic methods.
