Search Results for author: Juho Lee

Found 39 papers, 18 papers with code

Diversity Matters When Learning From Ensembles

no code implementations NeurIPS 2021 Giung Nam, Jongmin Yoon, Yoonho Lee, Juho Lee

We propose a simple approach for reducing this gap, i. e., making the distilled performance close to the full ensemble.

Image Classification

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

no code implementations12 Oct 2021 Jeffrey Ryan Willette, Hae Beom Lee, Juho Lee, Sung Ju Hwang

Numerous recent works utilize bi-Lipschitz regularization of neural network layers to preserve relative distances between data instances in the feature spaces of each layer.

Meta-Learning

Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning

no code implementations6 Oct 2021 Seanie Lee, Hae Beom Lee, Juho Lee, Sung Ju Hwang

Thanks to the gradients aligned between tasks by our method, the model becomes less vulnerable to negative transfer and catastrophic forgetting.

Continual Learning Multi-Task Learning +1

Assumption-Free Survival Analysis Under Local Smoothness Prior

no code implementations29 Sep 2021 Seungjae Jung, Min-Kyu Kim, Juho Lee, Young-Jin Park, Nahyeon Park, Kyung-Min Kim

Survival analysis appears in various fields such as medicine, economics, engineering, and business.

Survival Analysis

Scale Mixtures of Neural Network Gaussian Processes

2 code implementations3 Jul 2021 Hyungi Lee, Eunggu Yun, Hongseok Yang, Juho Lee

We show that simply introducing a scale prior on the last-layer parameters can turn infinitely-wide neural networks of any architecture into a richer class of stochastic processes.

Gaussian Processes

Learning to Pool in Graph Neural Networks for Extrapolation

no code implementations11 Jun 2021 Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee

However, according to a recent study, a careful choice of pooling functions, which are used for the aggregation and readout operations in GNNs, is crucial for enabling GNNs to extrapolate.

Adversarial purification with Score-based generative models

1 code implementation11 Jun 2021 Jongmin Yoon, Sung Ju Hwang, Juho Lee

Recently, an Energy-Based Model (EBM) trained with Markov-Chain Monte-Carlo (MCMC) has been highlighted as a purification model, where an attacked image is purified by running a long Markov-chain using the gradients of the EBM.

Denoising

Hybrid Generative-Contrastive Representation Learning

1 code implementation11 Jun 2021 Saehoon Kim, Sungwoong Kim, Juho Lee

On the other hand, the generative pre-training directly estimates the data distribution, so the representations tend to be robust but not optimal for discriminative tasks.

Contrastive Learning Unsupervised Representation Learning

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

1 code implementation ACL 2021 Seanie Lee, Minki Kang, Juho Lee, Sung Ju Hwang

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 falls outside the training distribution, due to distributional shifts. Data augmentation (DA) techniques which drop/replace words have shown to be effective in regularizing the model from overfitting to the training data. Yet, they may adversely affect the QA tasks since they incur semantic changes that may lead to wrong answers for the QA task.

Data Augmentation Domain Generalization +1

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

2 code implementations CVPR 2021 Jinwoo Kim, Jaehoon Yoo, Juho Lee, Seunghoon Hong

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.

Point Cloud Generation

Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding

no code implementations NeurIPS 2021 Bruno Andreis, Jeffrey Willette, Juho Lee, Sung Ju Hwang

The proposed method adheres to the required symmetries of invariance and equivariance as well as maintaining MBC for any partition of the input set.

Improving Uncertainty Calibration via Prior Augmented Data

no code implementations22 Feb 2021 Jeffrey Willette, Juho Lee, Sung Ju Hwang

Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators.

A Multi-Mode Modulator for Multi-Domain Few-Shot Classification

no code implementations ICCV 2021 Yanbin Liu, Juho Lee, Linchao Zhu, Ling Chen, Humphrey Shi, Yi Yang

Most existing few-shot classification methods only consider generalization on one dataset (i. e., single-domain), failing to transfer across various seen and unseen domains.

Classification Domain Generalization

Improving Neural Network Accuracy and Calibration Under Distributional Shift with Prior Augmented Data

no code implementations1 Jan 2021 Jeffrey Ryan Willette, Juho Lee, Sung Ju Hwang

We demonstrate the effectiveness of our method and validate its performance on both classification and regression problems by applying it to the training of recent state-of-the-art neural network models.

Adaptive Strategy for Resetting a Non-stationary Markov Chain during Learning via Joint Stochastic Approximation

no code implementations pproximateinference AABI Symposium 2021 Hyunsu Kim, Juho Lee, Hongseok Yang

The non-stationary kernel problem refers to the degraded performance of the algorithm due to the constant change of the transition kernel of the chain throughout the run of the algorithm.

Amortized Probabilistic Detection of Communities in Graphs

2 code implementations29 Oct 2020 Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman

While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.

Community Detection

Neural Complexity Measures

1 code implementation NeurIPS 2020 Yoonho Lee, Juho Lee, Sung Ju Hwang, Eunho Yang, Seungjin Choi

While various complexity measures for deep neural networks exist, specifying an appropriate measure capable of predicting and explaining generalization in deep networks has proven challenging.

Meta-Learning

Bootstrapping Neural Processes

1 code implementation NeurIPS 2020 Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh

While this "data-driven" way of learning stochastic processes has proven to handle various types of data, NPs still rely on an assumption that uncertainty in stochastic processes is modeled by a single latent variable, which potentially limits the flexibility.

Stochastic Subset Selection for Efficient Training and Inference of Neural Networks

no code implementations25 Jun 2020 Bruno Andreis, A. Tuan Nguyen, Seanie Lee, Juho Lee, Eunho Yang, Sung Ju Hwang

We also show in our experiments that our method enhances scalability of nonparametric models such as Neural Processes.

Feature Selection Meta-Learning

Cost-effective Interactive Attention Learning with Neural Attention Processes

2 code implementations9 Jun 2020 Jay Heo, Junhyeon Park, Hyewon Jeong, Kwang Joon Kim, Juho Lee, Eunho Yang, Sung Ju Hwang

Moreover, it is almost infeasible for the human annotators to examine attentions on tons of instances and features.

Time Series

Graph Embedding VAE: A Permutation Invariant Model of Graph Structure

no code implementations17 Oct 2019 Tony Duan, Juho Lee

Generative models of graph structure have applications in biology and social sciences.

Graph Embedding Graph Generation

Deep Amortized Clustering

no code implementations ICLR 2020 Juho Lee, Yoonho Lee, Yee Whye Teh

We propose a deep amortized clustering (DAC), a neural architecture which learns to cluster datasets efficiently using a few forward passes.

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

no code implementations26 May 2019 Juho Lee, Xenia Miscouridou, François Caron

In particular, we show that one can get novel series representations for the generalized gamma process and the stable beta process.

Density Estimation Survival Analysis

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

1 code implementation13 Feb 2019 Fadhel Ayed, Juho Lee, François Caron

Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated two-parameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior.

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

1 code implementation3 Oct 2018 Juho Lee, Lancelot F. James, Seungjin Choi, François Caron

We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties.

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

4 code implementations1 Oct 2018 Juho Lee, Yoonho Lee, Jungtaek Kim, Adam R. Kosiorek, Seungjin Choi, Yee Whye Teh

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.

3D Shape Recognition Few-Shot Image Classification +1

ADAPTIVE NETWORK SPARSIFICATION VIA DEPENDENT VARIATIONAL BETA-BERNOULLI DROPOUT

no code implementations27 Sep 2018 Juho Lee, Saehoon Kim, Jaehong Yoon, Hae Beom Lee, Eunho Yang, Sung Ju Hwang

With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss.

Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare

2 code implementations5 Jun 2018 Ingyo Chung, Saehoon Kim, Juho Lee, Kwang Joon Kim, Sung Ju Hwang, Eunho Yang

We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention.

Gaussian Processes Time Series

Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout

1 code implementation28 May 2018 Juho Lee, Saehoon Kim, Jaehong Yoon, Hae Beom Lee, Eunho Yang, Sung Ju Hwang

With such input-independent dropout, each neuron is evolved to be generic across inputs, which makes it difficult to sparsify networks without accuracy loss.

DropMax: Adaptive Stochastic Softmax

no code implementations ICLR 2018 Hae Beom Lee, Juho Lee, Eunho Yang, Sung Ju Hwang

Moreover, the learning of dropout probabilities for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes.

Classification General Classification +1

DropMax: Adaptive Variational Softmax

4 code implementations NeurIPS 2018 Hae Beom Lee, Juho Lee, Saehoon Kim, Eunho Yang, Sung Ju Hwang

Moreover, the learning of dropout rates for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes.

Classification General Classification +1

Bayesian inference on random simple graphs with power law degree distributions

no code implementations ICML 2017 Juho Lee, Creighton Heaukulani, Zoubin Ghahramani, Lancelot F. James, Seungjin Choi

The BFRY random variables are well approximated by gamma random variables in a variational Bayesian inference routine, which we apply to several network datasets for which power law degree distributions are a natural assumption.

Bayesian Inference

Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models

no code implementations NeurIPS 2016 Juho Lee, Lancelot F. James, Seungjin Choi

Bayesian nonparametric methods based on the Dirichlet process (DP), gamma process and beta process, have proven effective in capturing aspects of various datasets arising in machine learning.

Bayesian Inference

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

no code implementations NeurIPS 2015 Juho Lee, Seungjin Choi

Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models.

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

no code implementations29 Jan 2015 Juho Lee, Seungjin Choi

Bayesian hierarchical clustering (BHC) is an agglomerative clustering method, where a probabilistic model is defined and its marginal likelihoods are evaluated to decide which clusters to merge.

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