Search Results for author: Hyunsin Park

Found 8 papers, 0 papers with code

Federated Learning of User Verification Models Without Sharing Embeddings

no code implementations18 Apr 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

SubSpectral Normalization for Neural Audio Data Processing

no code implementations25 Mar 2021 Simyung Chang, Hyoungwoo Park, Janghoon Cho, Hyunsin Park, Sungrack Yun, Kyuwoong Hwang

In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group.

 Ranked #1 on Keyword Spotting on Google Speech Commands (% Test Accuracy metric)

Affine Transformation Keyword Spotting

Secure Federated Learning of User Verification Models

no code implementations1 Jan 2021 Hossein Hosseini, Hyunsin Park, Sungrack Yun, Christos Louizos, Joseph Soriaga, Max Welling

We consider the problem of training User Verification (UV) models in federated setup, where the conventional loss functions are not applicable due to the constraints that each user has access to the data of only one class and user embeddings cannot be shared with the server or other users.

Federated Learning

Federated Learning of User Authentication Models

no code implementations9 Jul 2020 Hossein Hosseini, Sungrack Yun, Hyunsin Park, Christos Louizos, Joseph Soriaga, Max Welling

In this paper, we propose Federated User Authentication (FedUA), a framework for privacy-preserving training of UA models.

Federated Learning Speaker Verification

Meta-Learning via Feature-Label Memory Network

no code implementations19 Oct 2017 Dawit Mureja, Hyunsin Park, Chang D. Yoo

The feature memory is used to store the features of input data samples and the label memory stores their labels.

Meta-Learning

Early Improving Recurrent Elastic Highway Network

no code implementations14 Aug 2017 Hyunsin Park, Chang D. Yoo

Expanding on the idea of adaptive computation time (ACT), with the use of an elastic gate in the form of a rectified exponentially decreasing function taking on as arguments as previous hidden state and input, the proposed model is able to evaluate the appropriate recurrent depth for each input.

Activity Recognition Language Modelling

Face Alignment Using Cascade Gaussian Process Regression Trees

no code implementations CVPR 2015 Donghoon Lee, Hyunsin Park, Chang D. Yoo

Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization.

Face Alignment

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.

General Classification

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