1 citations · 2 across the 16 of their papers we have counts for
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Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
Namhyoung Kim, Jae Wook Song
Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited fl…
PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor
Jaewon Jung, Hongsun Jang, Jaeyong Song +1
Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains. In this situation, adversarial distillation is a promising op…
GraNNDis: Efficient Unified Distributed Training Framework for Deep GNNs on Large Clusters
Jaeyong Song, Hongsun Jang, Jaewon Jung +2
Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training…
Pipe-BD: Pipelined Parallel Blockwise Distillation
Hongsun Jang, Jaewon Jung, Jaeyong Song +3
Training large deep neural network models is highly challenging due to their tremendous computational and memory requirements. Blockwise distillation provides one promising method…
SGCN: Exploiting Compressed-Sparse Features in Deep Graph Convolutional Network Accelerators
Mingi Yoo, Jaeyong Song, Jounghoo Lee +3
Graph convolutional networks (GCNs) are becoming increasingly popular as they overcome the limited applicability of prior neural networks. A GCN takes as input an arbitrarily struc…
Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression
Jaeyong Song, Jinkyu Yim, Jaewon Jung +4
In training of modern large natural language processing (NLP) models, it has become a common practice to split models using 3D parallelism to multiple GPUs. Such technique, however…
Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators
Mingi Yoo, Jaeyong Song, Hyeyoon Lee +4
Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One…