26 citations · 268 across the 48 of their papers we have counts for
11 papers · 1 filter
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization
Jung Hyun Lee, Jihun Yun, Sung Ju Hwang +1
Network quantization, which aims to reduce the bit-lengths of the network weights and activations, has emerged as one of the key ingredients to reduce the size of neural networks f…
Self-supervised Label Augmentation via Input Transformations
Hankook Lee, Sung Ju Hwang, Jinwoo Shin
Self-supervised learning, which learns by constructing artificial labels given only the input signals, has recently gained considerable attention for learning representations with…
Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection
Byunggill Joe, Sung Ju Hwang, Insik Shin
Although deep neural networks have shown promising performances on various tasks, even achieving human-level performance on some, they are shown to be susceptible to incorrect pred…
Learning to Generalize to Unseen Tasks with Bilevel Optimization
Hayeon Lee, Donghyun Na, Hae Beom Lee +1
Recent metric-based meta-learning approaches, which learn a metric space that generalizes well over combinatorial number of different classification tasks sampled from a task distr…
Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challengi…
Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck
Sungyub Kim, Yongsu Baek, Sung Ju Hwang +1
Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and is commonly considered in diverse mis…