Search Results for author: Taesup Kim

Found 14 papers, 5 papers with code

Task-Agnostic Continual Reinforcement Learning: In Praise of a Simple Baseline

no code implementations28 May 2022 Massimo Caccia, Jonas Mueller, Taesup Kim, Laurent Charlin, Rasool Fakoor

We study task-agnostic continual reinforcement learning (TACRL) in which standard RL challenges are compounded with partial observability stemming from task agnosticism, as well as additional difficulties of continual learning (CL), i. e., learning on a non-stationary sequence of tasks.

Continual Learning Continuous Control +2

Deep Quantile Aggregation

no code implementations26 Feb 2021 Taesup Kim, Rasool Fakoor, Jonas Mueller, Ryan J. Tibshirani, Alexander J. Smola

Conditional quantile estimation is a key statistical learning challenge motivated by the need to quantify uncertainty in predictions or to model a diverse population without being overly reductive.

Visual Concept Reasoning Networks

no code implementations26 Aug 2020 Taesup Kim, Sungwoong Kim, Yoshua Bengio

It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties.

Action Recognition Image Classification +4

Variational Temporal Abstraction

2 code implementations NeurIPS 2019 Taesup Kim, Sungjin Ahn, Yoshua Bengio

We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data.

Scalable Neural Architecture Search for 3D Medical Image Segmentation

no code implementations13 Jun 2019 Sungwoong Kim, Ildoo Kim, Sungbin Lim, Woonhyuk Baek, Chiheon Kim, Hyungjoo Cho, Boogeon Yoon, Taesup Kim

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.

Medical Image Segmentation Neural Architecture Search +1

Edge-labeling Graph Neural Network for Few-shot Learning

3 code implementations CVPR 2019 Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo

In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning.

Few-Shot Image Classification

Fast AutoAugment

10 code implementations NeurIPS 2019 Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, Sungwoong Kim

Data augmentation is an essential technique for improving generalization ability of deep learning models.

Image Augmentation Image Classification

Bayesian Model-Agnostic Meta-Learning

2 code implementations NeurIPS 2018 Taesup Kim, Jaesik Yoon, Ousmane Dia, Sungwoong Kim, Yoshua Bengio, Sungjin Ahn

Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.

Active Learning Image Classification +3

PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples

1 code implementation ICLR 2018 Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, Nate Kushman

Adversarial perturbations of normal images are usually imperceptible to humans, but they can seriously confuse state-of-the-art machine learning models.

Two-sample testing

Dynamic Layer Normalization for Adaptive Neural Acoustic Modeling in Speech Recognition

no code implementations19 Jul 2017 Taesup Kim, Inchul Song, Yoshua Bengio

Layer normalization is a recently introduced technique for normalizing the activities of neurons in deep neural networks to improve the training speed and stability.

Speech Recognition

Deep Directed Generative Models with Energy-Based Probability Estimation

no code implementations10 Jun 2016 Taesup Kim, Yoshua Bengio

Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training.

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