179 citations · 1k across the 84 of their papers we have counts for
58 papers · 1 filter
Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts
Dohyeon Kim, Bedionita Soro, Sung Ju Hwang
Mixture-of-Experts (MoE) architectures have emerged as a powerful paradigm for scaling model capacity while preserving efficient inference in large foundation models. However, most…
Continual Learners are Incremental Model Generalizers
Jaehong Yoon, Sung Ju Hwang, Yue Cao
Motivated by the efficiency and rapid convergence of pre-trained models for solving downstream tasks, this paper extensively studies the impact of Continual Learning (CL) models as…
Meta-prediction Model for Distillation-Aware NAS on Unseen Datasets
Hayeon Lee, Sohyun An, Minseon Kim +1
Distillation-aware Neural Architecture Search (DaNAS) aims to search for an optimal student architecture that obtains the best performance and/or efficiency when distilling the kno…
Set-based Neural Network Encoding Without Weight Tying
Bruno Andreis, Soro Bedionita, Philip H. S. Torr +1
We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network paramet…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…
Forget-free Continual Learning with Soft-Winning SubNetworks
Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we…
Graph Generation with Diffusion Mixture
Jaehyeong Jo, Dongki Kim, Sung Ju Hwang
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achie…
Learning Transferable Adversarial Robust Representations via Multi-view Consistency
Minseon Kim, Hyeonjeong Ha, Dong Bok Lee +1
Despite the success on few-shot learning problems, most meta-learned models only focus on achieving good performance on clean examples and thus easily break down when given adversa…
Effective Targeted Attacks for Adversarial Self-Supervised Learning
Minseon Kim, Hyeonjeong Ha, Sooel Son +1
Recently, unsupervised adversarial training (AT) has been highlighted as a means of achieving robustness in models without any label information. Previous studies in unsupervised A…
On the Soft-Subnetwork for Few-shot Class Incremental Learning
Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive pe…
Dataset Condensation with Latent Space Knowledge Factorization and Sharing
Hae Beom Lee, Dong Bok Lee, Sung Ju Hwang
In this paper, we introduce a novel approach for systematically solving dataset condensation problem in an efficient manner by exploiting the regularity in a given dataset. Instead…
Personalized Subgraph Federated Learning
Jinheon Baek, Wonyong Jeong, Jiongdao Jin +2
Subgraphs of a larger global graph may be distributed across multiple devices, and only locally accessible due to privacy restrictions, although there may be links between subgraph…
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
Jaehong Yoon, Geon Park, Wonyong Jeong +1
In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made i…
Graph Self-supervised Learning with Accurate Discrepancy Learning
Dongki Kim, Jinheon Baek, Sung Ju Hwang
Self-supervised learning of graph neural networks (GNNs) aims to learn an accurate representation of the graphs in an unsupervised manner, to obtain transferable representations of…
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang
Generating graph-structured data requires learning the underlying distribution of graphs. Yet, this is a challenging problem, and the previous graph generative methods either fail…
Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching
Wonyong Jeong, Sung Ju Hwang
In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label…
Representational Continuity for Unsupervised Continual Learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li +2
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual l…
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Hankook Lee, Sungsoo Ahn, Seung-Woo Seo +4
Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have…
Edge Representation Learning with Hypergraphs
Jaehyeong Jo, Jinheon Baek, Seul Lee +3
Graph neural networks have recently achieved remarkable success in representing graph-structured data, with rapid progress in both the node embedding and graph pooling methods. Yet…
Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding
Bruno Andreis, Jeffrey Willette, Juho Lee +1
Most existing set encoding algorithms operate under the implicit assumption that all the set elements are accessible, and that there are ample computational and memory resources to…
Task-Adaptive Neural Network Search with Meta-Contrastive Learning
Wonyong Jeong, Hayeon Lee, Gun Park +3
Most conventional Neural Architecture Search (NAS) approaches are limited in that they only generate architectures without searching for the optimal parameters. While some NAS meth…
Improving Uncertainty Calibration via Prior Augmented Data
Jeffrey Willette, Juho Lee, Sung Ju Hwang
Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. However, they are often overconfident in their pre…
Accurate Learning of Graph Representations with Graph Multiset Pooling
Jinheon Baek, Minki Kang, Sung Ju Hwang
Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks. Yet, obtaining an accurate repres…
Large-Scale Meta-Learning with Continual Trajectory Shifting
Jaewoong Shin, Hae Beom Lee, Boqing Gong +1
Meta-learning of shared initialization parameters has shown to be highly effective in solving few-shot learning tasks. However, extending the framework to many-shot scenarios, whic…