b'Juho Lee'

research

∙ 08/21/2023

Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs

Real-world graphs are dynamic, constantly evolving with new interactions...

0 Dongjin Lee, et al. ∙

research

∙ 08/13/2023

Probabilistic Imputation for Time-series Classification with Missing Data

Multivariate time series data for real-world applications typically cont...

0 SeungHyun Kim, et al. ∙

research

∙ 07/12/2023

Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion Models

Large-scale image generation models, with impressive quality made possib...

0 Sanghyun Kim, et al. ∙

research

∙ 06/20/2023

Traversing Between Modes in Function Space for Fast Ensembling

Deep ensemble is a simple yet powerful way to improve the performance of...

0 Eunggu Yun, et al. ∙

research

∙ 06/01/2023

Regularizing Towards Soft Equivariance Under Mixed Symmetries

Datasets often have their intrinsic symmetries, and particular deep-lear...

0 Hyunsu Kim, et al. ∙

research

∙ 05/24/2023

SWAMP: Sparse Weight Averaging with Multiple Particles for Iterative Magnitude Pruning

Given the ever-increasing size of modern neural networks, the significan...

0 Moonseok Choi, et al. ∙

research

∙ 04/19/2023

Martingale Posterior Neural Processes

A Neural Process (NP) estimates a stochastic process implicitly defined ...

0 Hyungi Lee, et al. ∙

research

∙ 04/19/2023

Decoupled Training for Long-Tailed Classification With Stochastic Representations

Decoupling representation learning and classifier learning has been show...

0 Giung Nam, et al. ∙

research

∙ 04/11/2023

Bayesian Analysis of Generalized Hierarchical Indian Buffet Processes for Within and Across Group Sharing of Latent Features

Bayesian nonparametric hierarchical priors provide flexible models for s...

0 Lancelot Fitzgerald James, et al. ∙

research

∙ 03/13/2023

5G-Advanced Towards 6G: Past, Present, and Future

Since the start of 5G work in 3GPP in early 2016, tremendous progress ha...

0 Wanshi Chen, et al. ∙

research

∙ 02/02/2023

Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning

We consider the optimisation of large and shallow neural networks via gr...

0 François Caron, et al. ∙

research

∙ 10/12/2022

On Divergence Measures for Bayesian Pseudocoresets

A Bayesian pseudocoreset is a small synthetic dataset for which the post...

17 Balhae Kim, et al. ∙

research

∙ 10/05/2022

Exploring The Role of Mean Teachers in Self-supervised Masked Auto-Encoders

Masked image modeling (MIM) has become a popular strategy for self-super...

24 Youngwan Lee, et al. ∙

research

∙ 08/26/2022

Universal Mini-Batch Consistency for Set Encoding Functions

Previous works have established solid foundations for neural set functio...

0 Jeffrey Willette, et al. ∙

research

∙ 06/30/2022

Improving Ensemble Distillation With Weight Averaging and Diversifying Perturbation

Ensembles of deep neural networks have demonstrated superior performance...

0 Giung Nam, et al. ∙

research

∙ 05/20/2022

Set-based Meta-Interpolation for Few-Task Meta-Learning

Meta-learning approaches enable machine learning systems to adapt to new...

0 Seanie Lee, et al. ∙

research

∙ 05/17/2022

Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility

This article studies the infinite-width limit of deep feedforward neural...

29 Hoil Lee, et al. ∙

research

∙ 02/02/2022

PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems

In multiple-input multiple-output (MIMO) systems, the high-resolution ch...

0 Pranav Madadi, et al. ∙

research

∙ 10/27/2021

Diversity Matters When Learning From Ensembles

Deep ensembles excel in large-scale image classification tasks both in t...

0 Giung Nam, et al. ∙

research

∙ 10/12/2021

Meta Learning Low Rank Covariance Factors for Energy-Based Deterministic Uncertainty

Numerous recent works utilize bi-Lipschitz regularization of neural netw...

0 Jeffrey Ryan Willette, et al. ∙

research

∙ 10/06/2021

Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning

Multilingual models jointly pretrained on multiple languages have achiev...

0 Seanie Lee, et al. ∙

research

∙ 07/03/2021

Scale Mixtures of Neural Network Gaussian Processes

Recent works have revealed that infinitely-wide feed-forward or recurren...

0 Hyungi Lee, et al. ∙

research

∙ 06/11/2021

Learning to Pool in Graph Neural Networks for Extrapolation

Graph neural networks (GNNs) are one of the most popular approaches to u...

0 Jihoon Ko, et al. ∙

research

∙ 06/11/2021

Adversarial purification with Score-based generative models

While adversarial training is considered as a standard defense method ag...

0 Jongmin Yoon, et al. ∙

research

∙ 06/11/2021

Hybrid Generative-Contrastive Representation Learning

Unsupervised representation learning has recently received lots of inter...

0 Saehoon Kim, et al. ∙

research

∙ 05/06/2021

Learning to Perturb Word Embeddings for Out-of-distribution QA

QA models based on pretrained language mod-els have achieved remarkable ...

0 Seanie Lee, et al. ∙

research

∙ 03/29/2021

SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

Generative modeling of set-structured data, such as point clouds, requir...

17 Jinwoo Kim, et al. ∙

research

∙ 03/21/2021

Posterior distributions for Hierarchical Spike and Slab Indian Buffet processes

Bayesian nonparametric hierarchical priors are highly effective in provi...

0 Lancelot F. James, et al. ∙

research

∙ 03/02/2021

Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding

Most existing set encoding algorithms operate under the assumption that ...

0 Bruno Andreis, et al. ∙

research

∙ 02/22/2021

Improving Uncertainty Calibration via Prior Augmented Data

Neural networks have proven successful at learning from complex data dis...

0 Jeffrey Willette, et al. ∙

research

∙ 10/29/2020

Attentive Clustering Processes

Amortized approaches to clustering have recently received renewed attent...

11 Ari Pakman, et al. ∙

research

∙ 08/07/2020

Bootstrapping Neural Processes

Unlike in the traditional statistical modeling for which a user typicall...

1 Juho Lee, et al. ∙

research

∙ 08/07/2020

Neural Complexity Measures

While various complexity measures for diverse model classes have been pr...

24 Yoonho Lee, et al. ∙

research

∙ 06/25/2020

Stochastic Subset Selection

Current machine learning algorithms are designed to work with huge volum...

24 Tuan A. Nguyen, et al. ∙

research

∙ 06/19/2020

The Normal-Generalised Gamma-Pareto process: A novel pure-jump Lévy process with flexible tail and jump-activity properties

Pure-jump Lévy processes are popular classes of stochastic processes whi...

0 Fadhel Ayed, et al. ∙

research

∙ 06/09/2020

Cost-effective Interactive Attention Learning with Neural Attention Processes

We propose a novel interactive learning framework which we refer to as I...

0 Jay Heo, et al. ∙

research

∙ 10/17/2019

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

Generative models of graph structure have applications in biology and so...

0 Tony Duan, et al. ∙

research

∙ 09/30/2019

Deep Amortized Clustering

We propose a deep amortized clustering (DAC), a neural architecture whic...

30 Juho Lee, et al. ∙

research

∙ 05/26/2019

A unified construction for series representations and finite approximations of completely random measures

Infinite-activity completely random measures (CRMs) have become importan...

0 Juho Lee, et al. ∙

research

∙ 02/13/2019

Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior

Bayesian nonparametric approaches, in particular the Pitman-Yor process ...

0 Fadhel Ayed, et al. ∙

research

∙ 10/03/2018

A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure

We consider a non-projective class of inhomogeneous random graph models ...

0 Juho Lee, et al. ∙

research

∙ 10/01/2018

Set Transformer

Many machine learning tasks such as multiple instance learning, 3D shape...

10 Juho Lee, et al. ∙

research

∙ 06/05/2018

Mixed Effect Composite RNN-GP: A Personalized and Reliable Prediction Model for Healthcare

We present a personalized and reliable prediction model for healthcare, ...

0 Ingyo Chung, et al. ∙

research

∙ 05/28/2018

Adaptive Network Sparsification via Dependent Variational Beta-Bernoulli Dropout

While variational dropout approaches have been shown to be effective for...

0 Juho Lee, et al. ∙

research

∙ 05/25/2018

Transductive Propagation Network for Few-shot Learning

Few-shot learning aims to build a learner that quickly generalizes to no...

0 Yanbin Liu, et al. ∙

research

∙ 05/24/2018

Uncertainty-Aware Attention for Reliable Interpretation and Prediction

Attention mechanism is effective in both focusing the deep learning mode...

0 Jay Heo, et al. ∙

research

∙ 12/21/2017

DropMax: Adaptive Stochastic Softmax

We propose DropMax, a stochastic version of softmax classifier which at ...

0 Hae Beom Lee, et al. ∙

research

∙ 02/27/2017

Bayesian inference on random simple graphs with power law degree distributions

We present a model for random simple graphs with a degree distribution t...

0 Juho Lee, et al. ∙

research

∙ 11/18/2015

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

Normalized random measures (NRMs) provide a broad class of discrete rand...

0 Juho Lee, et al. ∙

research

∙ 01/29/2015

Bayesian Hierarchical Clustering with Exponential Family: Small-Variance Asymptotics and Reducibility

Bayesian hierarchical clustering (BHC) is an agglomerative clustering me...

0 Juho Lee, et al. ∙

Juho Lee

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