Search Results for author: Minju Jung

Found 10 papers, 2 papers with code

KuraNet: Systems of Coupled Oscillators that Learn to Synchronize

1 code implementation6 May 2021 Matthew Ricci, Minju Jung, Yuwei Zhang, Mathieu Chalvidal, Aneri Soni, Thomas Serre

Here, we present a single approach to both of these problems in the form of "KuraNet", a deep-learning-based system of coupled oscillators that can learn to synchronize across a distribution of disordered network conditions.

An empirical study of a pruning mechanism

1 code implementation1 Jan 2021 Minju Jung, Hyounguk Shon, Eojindl Yi, SungHyun Baek, Junmo Kim

For the pruning and retraining phase, whether the pruned-and-retrained network benefits from the pretrained network indded is examined.

Association Network Pruning

Goal-Directed Behavior under Variational Predictive Coding: Dynamic Organization of Visual Attention and Working Memory

no code implementations12 Mar 2019 Minju Jung, Takazumi Matsumoto, Jun Tani

Furthermore, our analysis of comparative experiments indicated that introduction of visual working memory and the inference mechanism using variational Bayes predictive coding significantly improve the performance in planning adequate goal-directed actions.

Bayesian Inference

Generating Goal-Directed Visuomotor Plans Based on Learning Using a Predictive Coding-type Deep Visuomotor Recurrent Neural Network Model

no code implementations7 Mar 2018 Minkyu Choi, Takazumi Matsumoto, Minju Jung, Jun Tani

The current paper presents how a predictive coding type deep recurrent neural networks can generate vision-based goal-directed plans based on prior learning experience by examining experiment results using a real arm robot.

Less-forgetful Learning for Domain Expansion in Deep Neural Networks

no code implementations16 Nov 2017 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain.

Domain Adaptation Image Classification

Adaptive Detrending to Accelerate Convolutional Gated Recurrent Unit Training for Contextual Video Recognition

no code implementations24 May 2017 Minju Jung, Haanvid Lee, Jun Tani

In this paper, inspired by the normalization and detrending methods, we propose adaptive detrending (AD) for temporal normalization in order to accelerate the training of ConvRNNs, especially for convolutional gated recurrent unit (ConvGRU).

Video Recognition

Less-forgetting Learning in Deep Neural Networks

no code implementations1 Jul 2016 Heechul Jung, Jeongwoo Ju, Minju Jung, Junmo Kim

Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments.

Recognition of Visually Perceived Compositional Human Actions by Multiple Spatio-Temporal Scales Recurrent Neural Networks

no code implementations5 Feb 2016 Haanvid Lee, Minju Jung, Jun Tani

The analysis of the internal representation obtained through the learning with the dataset clarifies what sorts of functional hierarchy can be developed by extracting the essential compositionality underlying the dataset.

Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination

no code implementations9 Jul 2015 Jungsik Hwang, Minju Jung, Naveen Madapana, Jinhyung Kim, Minkyu Choi, Jun Tani

The current study examines how adequate coordination among different cognitive processes including visual recognition, attention switching, action preparation and generation can be developed via learning of robots by introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).

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