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cs.DM2021
The Learning and Communication Complexity of Subsequence Containment
Mason DiCicco, Daniel Reichman
We consider the learning and communication complexity of subsequence containment. In the learning problem, we seek to learn a classifier that positively labels a binary string …
cs.LG2021★ 4 cited
Size and Depth Separation in Approximating Benign Functions with Neural Networks
Gal Vardi, Daniel Reichman, Toniann Pitassi +1
When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. How…