517 citations · 842 across the 38 of their papers we have counts for
41 papers
ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation
Changhun Kim, Sunguk Jang, Jeongjun Lee +5
Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynam…
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
Young-Jin Park, Donghyun Kim, Frédéric Odermatt +2
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization. Traditional time serie…
Self-Supervised Dataset Distillation for Transfer Learning
Dong Bok Lee, Seanie Lee, Joonho Ko +3
Dataset distillation methods have achieved remarkable success in distilling a large dataset into a small set of representative samples. However, they are not designed to produce a…
Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs
Dongjin Lee, Juho Lee, Kijung Shin
Real-world graphs are dynamic, constantly evolving with new interactions, such as financial transactions in financial networks. Temporal Graph Neural Networks (TGNNs) have been dev…
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
Francois Caron, Fadhel Ayed, Paul Jung +3
We consider gradient-based optimisation of wide, shallow neural networks, where the output of each hidden node is scaled by a positive parameter. The scaling parameters are non-ide…
On Divergence Measures for Bayesian Pseudocoresets
Balhae Kim, Jungwon Choi, Seanie Lee +3
A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesia…
Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders
Youngwan Lee, Jeffrey Willette, Jonghee Kim +2
Masked image modeling (MIM) has become a popular strategy for self-supervised learning~(SSL) of visual representations with Vision Transformers. A representative MIM model, the mas…
Self-Distillation for Further Pre-training of Transformers
Seanie Lee, Minki Kang, Juho Lee +2
Pre-training a large transformer model on a massive amount of unlabeled data and fine-tuning it on labeled datasets for diverse downstream tasks has proven to be a successful strat…
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation
Jeffrey Willette, Seanie Lee, Bruno Andreis +3
Recent work on mini-batch consistency (MBC) for set functions has brought attention to the need for sequentially processing and aggregating chunks of a partitioned set while guaran…
Set-based Meta-Interpolation for Few-Task Meta-Learning
Seanie Lee, Bruno Andreis, Kenji Kawaguchi +2
Meta-learning approaches enable machine learning systems to adapt to new tasks given few examples by leveraging knowledge from related tasks. However, a large number of meta-traini…
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Hoil Lee, Fadhel Ayed, Paul Jung +3
This article studies the infinite-width limit of deep feedforward neural networks whose weights are dependent, and modelled via a mixture of Gaussian distributions. Each hidden nod…
PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems
Pranav Madadi, Jeongho Jeon, Joonyoung Cho +3
In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in th…
Scale Mixtures of Neural Network Gaussian Processes
Hyungi Lee, Eunggu Yun, Hongseok Yang +1
Recent works have revealed that infinitely-wide feed-forward or recurrent neural networks of any architecture correspond to Gaussian processes referred to as Neural Network Gaussia…
Learning to Perturb Word Embeddings for Out-of-distribution QA
Seanie Lee, Minki Kang, Juho Lee +1
QA models based on pretrained language mod-els have achieved remarkable performance on various benchmark datasets.However, QA models do not generalize well to unseen data that fall…
Hybrid Generative-Contrastive Representation Learning
Saehoon Kim, Sungwoong Kim, Juho Lee
Unsupervised representation learning has recently received lots of interest due to its powerful generalizability through effectively leveraging large-scale unlabeled data. There ar…
Adversarial purification with Score-based generative models
Jongmin Yoon, Sung Ju Hwang, Juho Lee
While adversarial training is considered as a standard defense method against adversarial attacks for image classifiers, adversarial purification, which purifies attacked images in…
Learning to Pool in Graph Neural Networks for Extrapolation
Jihoon Ko, Taehyung Kwon, Kijung Shin +1
Graph neural networks (GNNs) are one of the most popular approaches to using deep learning on graph-structured data, and they have shown state-of-the-art performances on a variety…
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Jinwoo Kim, Jaehoon Yoo, Juho Lee +1
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks fo…
Posterior distributions for Hierarchical Spike and Slab Indian Buffet processes
Lancelot F. James, Juho Lee, Abhinav Pandey
Bayesian nonparametric hierarchical priors are highly effective in providing flexible models for latent data structures exhibiting sharing of information between and across groups.…
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…
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…
Amortized Probabilistic Detection of Communities in Graphs
Yueqi Wang, Yoonho Lee, Pallab Basu +4
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, exis…
Bootstrapping Neural Processes
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Unlike in the traditional statistical modeling for which a user typically hand-specify a prior, Neural Processes (NPs) implicitly define a broad class of stochastic processes with…
Neural Complexity Measures
Yoonho Lee, Juho Lee, Sung Ju Hwang +2
While various complexity measures for deep neural networks exist, specifying an appropriate measure capable of predicting and explaining generalization in deep networks has proven…