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Shallow Univariate ReLu Networks as Splines: Initialization, Loss Surface, Hessian, & Gradient Flow Dynamics
Our implicit regularization results are complementary to recent work arXiv:1906. 07842, done independently, which showed that initialization scale critically controls implicit regularization via a kernel-based argument.
A Functional Characterization of Randomly Initialized Gradient Descent in Deep ReLU Networks
Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems.
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems
We use deep value-based reinforcement learning to learn a pivoting strategy that at each iteration chooses between two of the most popular pivot rules -- Dantzig and steepest edge.
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