274 citations · 649 across the 21 of their papers we have counts for
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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…
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…
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong +1
Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have bee…
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
Sangwoo Mo, Minsu Cho, Jinwoo Shin
Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous…
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…