17 citations · 54 across the 15 of their papers we have counts for
5 papers · 2 filters
Provable Memorization via Deep Neural Networks using Sub-linear Parameters
Sejun Park, Jaeho Lee, Chulhee Yun +1
It is known that parameters are sufficient for neural networks to memorize arbitrary input-label pairs. By exploiting depth, we show that parameters suffice…
Layer-adaptive sparsity for the Magnitude-based Pruning
Jaeho Lee, Sejun Park, Sangwoo Mo +2
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sp…
Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning
Jaehyung Kim, Youngbum Hur, Sejun Park +3
While semi-supervised learning (SSL) has proven to be a promising way for leveraging unlabeled data when labeled data is scarce, the existing SSL algorithms typically assume that t…
Minimum Width for Universal Approximation
Sejun Park, Chulhee Yun, Jaeho Lee +1
The universal approximation property of width-bounded networks has been studied as a dual of classical universal approximation results on depth-bounded networks. However, the criti…
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning
Sejun Park, Jaeho Lee, Sangwoo Mo +1
Magnitude-based pruning is one of the simplest methods for pruning neural networks. Despite its simplicity, magnitude-based pruning and its variants demonstrated remarkable perform…