Edge-labeling Graph Neural Network for Few-shot Learning

4 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.

Clustering Few-Shot Image Classification +1

Automated Learning Rate Scheduler for Large-batch Training

1 code implementation • ICML Workshop AutoML 2021 • Chiheon Kim, Saehoon Kim, Jongmin Kim, Donghoon Lee, Sungwoong Kim

Large-batch training has been essential in leveraging large-scale datasets and models in deep learning.

Image Classification Scheduling

MAGVLT: Masked Generative Vision-and-Language Transformer

1 code implementation • CVPR 2023 • Sungwoong Kim, DaeJin Jo, Donghoon Lee, Jongmin Kim

Particularly, MAGVLT achieves competitive results on both zero-shot image-to-text and text-to-image generation tasks from MS-COCO by one moderate-sized model (fewer than 500M parameters) even without the use of monomodal data and networks.

Image Captioning Text Infilling +1

LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward

1 code implementation • 11 Oct 2022 • DaeJin Jo, Sungwoong Kim, Daniel Wontae Nam, Taehwan Kwon, Seungeun Rho, Jongmin Kim, Donghoon Lee

In order to resolve these issues, in this paper, we propose a learnable hash-based episodic count, which we name LECO, that efficiently performs as a task-specific intrinsic reward in hard exploration problems.

Efficient Exploration reinforcement-learning

Insights From the NeurIPS 2021 NetHack Challenge

1 code implementation • 22 Mar 2022 • Eric Hambro, Sharada Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michał Sypetkowski

In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.

NetHack Reinforcement Learning (RL)

Phoneme Classification using Constrained Variational Gaussian Process Dynamical System

no code implementations • NeurIPS 2012 • Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang D. Yoo

This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification.

Classification General Classification

HyPHEN: A Hybrid Packing Method and Optimizations for Homomorphic Encryption-Based Neural Networks

no code implementations • 5 Feb 2023 • Donghwan Kim, Jaiyoung Park, Jongmin Kim, Sangpyo Kim, Jung Ho Ahn

Convolutional neural network (CNN) inference using fully homomorphic encryption (FHE) is a promising private inference (PI) solution due to the capability of FHE that enables offloading the whole computation process to the server while protecting the privacy of sensitive user data.

NeuJeans: Private Neural Network Inference with Joint Optimization of Convolution and Bootstrapping

no code implementations • 7 Dec 2023 • Jae Hyung Ju, Jaiyoung Park, Jongmin Kim, Donghwan Kim, Jung Ho Ahn

NeuJeans accelerates the performance of conv2d by up to 5. 68 times compared to state-of-the-art FHE-based PI work and performs the PI of a CNN at the scale of ImageNet (ResNet18) within a mere few seconds