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On the influence of stochastic rounding bias in implementing gradient descent with applications in low-precision training – Lu Xia (Eindhoven University of Technology)

In the context of low-precision computation for the training of neural networks with thegradient descent method (GD), the occurrence of deterministic rounding errors often leadsto stagnation or adversely affects the convergence of the optimizers.…

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On Moduli of Quiver Representations and Applications to Geometry – Patricio Gallardo (University of California, Riverside)

The moduli of quiver representations, i.e. tuples of line maps arranged per a prescribed directed graph, serve as a key tool within geometry and representation theory. In this talk, we will describe their structure and explore their applications to…

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