72 citations · 330 across the 48 of their papers we have counts for
7 papers · 1 filter
MASKER: Masked Keyword Regularization for Reliable Text Classification
Seung Jun Moon, Sangwoo Mo, Kimin Lee +2
Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textu…
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…
Learning from Failure: Training Debiased Classifier from Biased Classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn +2
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased. While previous work tackles 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…
Learning Bounds for Risk-sensitive Learning
Jaeho Lee, Sejun Park, Jinwoo Shin
In risk-sensitive learning, one aims to find a hypothesis that minimizes a risk-averse (or risk-seeking) measure of loss, instead of the standard expected loss. In this paper, we p…
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…