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Provable Memorization via Deep Neural Networks using Sub-linear Parameters
Sejun Park, Jaeho Lee, Chulhee Yun, Jinwoo Shin
A Unifying View on Implicit Bias in Training Linear Neural Networks
Chulhee Yun, Shankar Krishnan, Hossein Mobahi
Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee, Jinwoo Shin
SGD with shuffling: optimal rates without component convexity and large epoch requirements
Kwangjun Ahn, Chulhee Yun, Suvrit Sra
$O(n)$ Connections are Expressive Enough: Universal Approximability of Sparse Transformers
Chulhee Yun, Yin-Wen Chang, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
Low-Rank Bottleneck in Multi-head Attention Models
Srinadh Bhojanapalli, Chulhee Yun, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
Are Transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar
Are deep ResNets provably better than linear predictors?
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun, Suvrit Sra, Ali Jadbabaie
Efficiently testing local optimality and escaping saddles for ReLU networks
Chulhee Yun, Suvrit Sra, Ali Jadbabaie