380 citations · 1.6k across the 84 of their papers we have counts for
89 papers
Mass Spectra Prediction with Structural Motif-based Graph Neural Networks
Jiwon Park, Jeonghee Jo, Sungroh Yoon
Mass spectra, which are agglomerations of ionized fragments from targeted molecules, play a crucial role across various fields for the identification of molecular structures. A pre…
On the Impact of Knowledge Distillation for Model Interpretability
Hyeongrok Han, Siwon Kim, Hyun-Soo Choi +1
Several recent studies have elucidated why knowledge distillation (KD) improves model performance. However, few have researched the other advantages of KD in addition to its improv…
New Insights for the Stability-Plasticity Dilemma in Online Continual Learning
Dahuin Jung, Dongjin Lee, Sunwon Hong +3
The aim of continual learning is to learn new tasks continuously (i.e., plasticity) without forgetting previously learned knowledge from old tasks (i.e., stability). In the scenari…
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks
Jaehee Jang, Heonseok Ha, Dahuin Jung +1
Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner…
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?
Jisoo Mok, Byunggook Na, Ji-Hoon Kim +2
In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass training of each…
AutoSNN: Towards Energy-Efficient Spiking Neural Networks
Byunggook Na, Jisoo Mok, Seongsik Park +3
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, there…
Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images
Sang-gil Lee, Eunji Kim, Jae Seok Bae +2
The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosi…
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising
Sungmin Cha, Seonwoo Min, Sungroh Yoon +1
We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the superv…
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
Heeseung Kim, Sungwon Kim, Sungroh Yoon
We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require any transcript of target speaker using classifier guidance. Guided-TTS combines an unconditio…
Scalable Smartphone Cluster for Deep Learning
Byunggook Na, Jaehee Jang, Seongsik Park +7
Various deep learning applications on smartphones have been rapidly rising, but training deep neural networks (DNNs) has too large computational burden to be executed on a single s…
Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation
Jungbeom Lee, Jooyoung Choi, Jisoo Mok +1
Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminati…
FICGAN: Facial Identity Controllable GAN for De-identification
Yonghyun Jeong, Jooyoung Choi, Sungwon Kim +5
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed…
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
Jooyoung Choi, Sungwon Kim, Yonghyun Jeong +2
Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in…
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate
Jongyoon Song, Sungwon Kim, Sungroh Yoon
Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition. Most recent appr…
Towards a Rigorous Evaluation of Time-series Anomaly Detection
Siwon Kim, Kukjin Choi, Hyun-Soo Choi +2
In recent years, proposed studies on time-series anomaly detection (TAD) report high F1 scores on benchmark TAD datasets, giving the impression of clear improvements in TAD. Howeve…
AdvRush: Searching for Adversarially Robust Neural Architectures
Jisoo Mok, Byunggook Na, Hyeokjun Choe +1
Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptib…
Toward Spatially Unbiased Generative Models
Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong +1
Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generator…
TargetNet: Functional microRNA Target Prediction with Deep Neural Networks
Seonwoo Min, Byunghan Lee, Sungroh Yoon
Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs…
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
Jeonghee Jo, Bumju Kwak, Byunghan Lee +1
A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent…
Accelerating Neural Architecture Search via Proxy Data
Byunggook Na, Jisoo Mok, Hyeokjun Choe +1
Despite the increasing interest in neural architecture search (NAS), the significant computational cost of NAS is a hindrance to researchers. Hence, we propose to reduce the cost o…
Training Energy-Efficient Deep Spiking Neural Networks with Time-to-First-Spike Coding
Seongsik Park, Sungroh Yoon
The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the hu…
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Bumju Kwak, Jiwon Park, Taewon Kang +3
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric…
Energy-efficient Knowledge Distillation for Spiking Neural Networks
Dongjin Lee, Seongsik Park, Jongwan Kim +2
Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation.…
Stein Latent Optimization for Generative Adversarial Networks
Uiwon Hwang, Heeseung Kim, Dahuin Jung +3
Generative adversarial networks (GANs) with clustered latent spaces can perform conditional generation in a completely unsupervised manner. In the real world, the salient attribute…