b'Eunho Yang'

research

∙ 08/21/2023

BackTrack: Robust template update via Backward Tracking of candidate template

Variations of target appearance such as deformations, illumination varia...

0 Dongwook Lee, et al. ∙

research

∙ 06/03/2023

SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization

Automatic speech recognition (ASR) models are frequently exposed to data...

0 Changhun Kim, et al. ∙

research

∙ 05/23/2023

ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models

Emotional Text-To-Speech (TTS) is an important task in the development o...

0 Minki Kang, et al. ∙

research

∙ 02/26/2023

Learning Input-agnostic Manipulation Directions in StyleGAN with Text Guidance

With the advantages of fast inference and human-friendly flexible manipu...

0 Yoonjeon Kim, et al. ∙

research

∙ 12/06/2022

Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding

Inspired by the impressive performance of recent face image editing meth...

0 Gyeongman Kim, et al. ∙

research

∙ 07/07/2022

Towards the Practical Utility of Federated Learning in the Medical Domain

Federated learning (FL) is an active area of research. One of the most s...

24 Seongjun Yang, et al. ∙

research

∙ 06/26/2022

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Learning unbiased node representations under class-imbalanced graph data...

0 Jaeyun Song, et al. ∙

research

∙ 12/16/2021

Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing

The Mixup scheme suggests mixing a pair of samples to create an augmente...

0 Joonhyung Park, et al. ∙

research

∙ 12/02/2021

Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation

Despite their remarkable ability to generalize with over-capacity networ...

0 Yeonsung Jung, et al. ∙

research

∙ 11/10/2021

Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation

Graph-structured datasets usually have irregular graph sizes and connect...

0 Joonhyung Park, et al. ∙

research

∙ 10/06/2021

Online Hyperparameter Meta-Learning with Hypergradient Distillation

Many gradient-based meta-learning methods assume a set of parameters tha...

0 Hae Beom Lee, et al. ∙

research

∙ 09/17/2021

Distilling Linguistic Context for Language Model Compression

A computationally expensive and memory intensive neural network lies beh...

0 Geondo Park, et al. ∙

research

∙ 09/05/2021

Cluster-Promoting Quantization with Bit-Drop for Minimizing Network Quantization Loss

Network quantization, which aims to reduce the bit-lengths of the networ...

0 Jung Hyun Lee, et al. ∙

research

∙ 07/01/2021

FedMix: Approximation of Mixup under Mean Augmented Federated Learning

Federated learning (FL) allows edge devices to collectively learn a mode...

0 Tehrim Yoon, et al. ∙

research

∙ 06/06/2021

Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

With rapid progress in neural text-to-speech (TTS) models, personalized ...

0 Dongchan Min, et al. ∙

research

∙ 06/02/2021

Online Coreset Selection for Rehearsal-based Continual Learning

A dataset is a shred of crucial evidence to describe a task. However, ea...

1 Jaehong Yoon, et al. ∙

research

∙ 05/03/2021

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning

Retrosynthesis, of which the goal is to find a set of reactants for synt...

0 Hankook Lee, et al. ∙

research

∙ 03/26/2021

Mutually-Constrained Monotonic Multihead Attention for Online ASR

Despite the feature of real-time decoding, Monotonic Multihead Attention...

0 Jaeyun Song, et al. ∙

research

∙ 02/07/2021

Model-Augmented Q-learning

In recent years, Q-learning has become indispensable for model-free rein...

0 Youngmin Oh, et al. ∙

research

∙ 10/28/2020

Attribution Preservation in Network Compression for Reliable Network Interpretation

Neural networks embedded in safety-sensitive applications such as self-d...

5 Geondo Park, 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

∙ 07/23/2020

Few-shot Visual Reasoning with Meta-analogical Contrastive Learning

While humans can solve a visual puzzle that requires logical reasoning b...

3 Youngsung Kim, et al. ∙

research

∙ 07/22/2020

Time-Reversal Symmetric ODE Network

Time-reversal symmetry, which requires that the dynamics of a system sho...

10 In Huh, et al. ∙

research

∙ 07/17/2020

Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning

While semi-supervised learning (SSL) has proven to be a promising way fo...

15 Jaehyung Kim, et al. ∙

research

∙ 07/15/2020

A General Family of Stochastic Proximal Gradient Methods for Deep Learning

We study the training of regularized neural networks where the regulariz...

0 Jihun Yun, et al. ∙

research

∙ 07/14/2020

Learning to Sample with Local and Global Contexts in Experience Replay Buffer

Experience replay, which enables the agents to remember and reuse experi...

7 Youngmin Oh, et al. ∙

research

∙ 07/02/2020

A Revision of Neural Tangent Kernel-based Approaches for Neural Networks

Recent theoretical works based on the neural tangent kernel (NTK) have s...

0 Kyung-Su Kim, et al. ∙

research

∙ 07/02/2020

Compressed Sensing via Measurement-Conditional Generative Models

A pre-trained generator has been frequently adopted in compressed sensin...

0 Kyung-Su Kim, 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

∙ 06/23/2020

Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

Although recent multi-task learning methods have shown to be effective i...

10 Tuan A. Nguyen, et al. ∙

research

∙ 06/22/2020

Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning

As deep neural networks are growing in size and being increasingly deplo...

2 Minyoung Song, et al. ∙

research

∙ 06/22/2020

Federated Semi-Supervised Learning with Inter-Client Consistency

While existing federated learning approaches mostly require that clients...

14 Wonyong Jeong, 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

∙ 03/06/2020

Federated Continual Learning with Adaptive Parameter Communication

There has been a surge of interest in continual learning and federated l...

1 Jaehong Yoon, et al. ∙

research

∙ 11/29/2019

Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bit-wise Regularization

Neural Network quantization, which aims to reduce bit-lengths of the net...

0 Jihun Yun, et al. ∙

research

∙ 06/07/2019

Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck

Estimating individual level treatment effects (ITE) from observational d...

0 Sungyub Kim, et al. ∙

research

∙ 06/01/2019

Sparsity Normalization: Stabilizing the Expected Outputs of Deep Networks

The learning of deep models, in which a numerous of parameters are super...

0 Joonyoung Yi, et al. ∙

research

∙ 05/30/2019

Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks

While tasks could come with varying number of instances in realistic set...

0 Donghyun Na, et al. ∙

research

∙ 05/30/2019

Meta Dropout: Learning to Perturb Features for Generalization

A machine learning model that generalizes well should obtain low errors ...

0 Hae Beom Lee, et al. ∙

research

∙ 05/26/2019

Stochastic Gradient Methods with Block Diagonal Matrix Adaptation

Adaptive gradient approaches that automatically adjust the learning rate...

0 Jihun Yun, et al. ∙

research

∙ 05/14/2019

Spectral Approximate Inference

Given a graphical model (GM), computing its partition function is the mo...

3 Sejun Park, et al. ∙

research

∙ 02/25/2019

ORACLE: Order Robust Adaptive Continual LEarning

The order of the tasks a continual learning model encounters may have la...

1 Jaehong Yoon, 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

∙ 05/24/2018

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

∙ 05/19/2018

M-estimation with the Trimmed l1 Penalty

We study high-dimensional M-estimators with the trimmed ℓ_1 penalty. Whi...

0 Jihun Yun, 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

∙ 10/04/2017

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Inferring predictive maps between multiple input and multiple output var...

0 Ming Yu, et al. ∙

research

∙ 09/18/2017

Why Pay More When You Can Pay Less: A Joint Learning Framework for Active Feature Acquisition and Classification

We consider the problem of active feature acquisition, where we sequenti...

0 Hajin Shim, et al. ∙

Eunho Yang

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