A framework quantifies DNN complexity via tensor operations, links 40 years of breakthroughs to complexity increases, and releases a dataset of 3000+ unexplored high-complexity architectures.
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Non-convex self-concordant functions enable regularized Newton and adaptive algorithms to achieve epsilon-approximate first-order stationary points in O(epsilon^{-2}) iterations with global convergence guarantees.
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On the Architectural Complexity of Neural Networks
A framework quantifies DNN complexity via tensor operations, links 40 years of breakthroughs to complexity increases, and releases a dataset of 3000+ unexplored high-complexity architectures.
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Non-Convex Self-Concordant Functions: Practical Algorithms and Complexity Analysis
Non-convex self-concordant functions enable regularized Newton and adaptive algorithms to achieve epsilon-approximate first-order stationary points in O(epsilon^{-2}) iterations with global convergence guarantees.