Supervised learning‐based DDoS attacks detection: Tuning hyperparameters

M Kim - ETRI Journal, 2019 - Wiley Online Library
Two supervised learning algorithms, a basic neural network and a long short‐term memory
recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of …

HyperTendril: Visual analytics for user-driven hyperparameter optimization of deep neural networks

H Park, Y Nam, JH Kim, J Choo - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
To mitigate the pain of manually tuning hyperparameters of deep neural networks,
automated machine learning (AutoML) methods have been developed to search for an …

[PDF][PDF] VisualHyperTuner: Visual analytics for user-driven hyperparameter tuning of deep neural networks

H Park, J Kim, M Kim, JH Kim, J Choo, JW Ha… - Demo at SysML …, 2019 - mlsys.org
Deep learning researchers and practitioners often struggle to find an optimal set of
hyperparameters to maximize model performance due to a large combinatorial search …

Hippo: sharing computations in hyper-parameter optimization

A Shin, JS Jeong, DY Kim, S Jung… - Proceedings of the VLDB …, 2022 - dl.acm.org
Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model
to its limits. However, a hyper-parameter optimization job, referred to as a study, involves …

[HTML][HTML] M2FN: Multi-step modality fusion for advertisement image assessment

KW Park, JW Ha, JH Lee, S Kwon, KM Kim… - Applied Soft …, 2021 - Elsevier
Assessing advertisements, specifically on the basis of user preferences and ad quality, is
crucial to the marketing industry. Although recent studies have attempted to use deep neural …

Accuracy-Time Efficient Hyperparameter Optimization Using Actor-Critic-based Reinforcement Learning and Early Stopping in OpenAI Gym Environment

AB Christian, CY Lin, YC Tseng, LD Van… - … on Internet of Things …, 2022 - ieeexplore.ieee.org
In this paper, we present accuracy-time efficient hyperparameter optimization (HPO) using
advantage actor-critic (A2C)-based reinforcement learning (RL) and early stopping in …

Resource-Aware Optimizations for Data-Intensive Systems

R Liu - 2023 - search.proquest.com
In modern cloud computing environments, ephemeral cloud resources are becoming
increasingly prevalent. Ephemeral resources exhibit two distinct characteristics:(1) they can …

[PDF][PDF] 네이버AI 플랫폼CLOVA 그리고초대규모

AI HyperCLOVA - koreascience.kr
력한 AI 도구 제공을 위해 그림 1 과 같이 음성인식, 합성, 컴퓨터비전, 자연어처리 등 다양한
분야의 AI 기술 및 서비스 개발을 진행중이다. 본 논문에서는 네이버 CLOVA 의 AI 연구 결과와 …

TreeML: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees

신안재 - 2021 - s-space.snu.ac.kr
Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model
to its limits. A hyper-parameter optimization job, referred to as a study, involves numerous …

[PDF][PDF] Suomenkielisten sanavektorien hyperparametrien optimointi

A Rossi - 2020 - helda.helsinki.fi
Sanavektoreita, eli eräänlaista sanakirjaa, jossa sanan määritelmänä on piste
moniulotteisessa avaruudessa, voidaan käyttää useissa luonnollisen kielen tehtävissä hyvin …