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 …
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
To mitigate the pain of manually tuning hyperparameters of deep neural networks,
automated machine learning (AutoML) methods have been developed to search for an …
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
Deep learning researchers and practitioners often struggle to find an optimal set of
hyperparameters to maximize model performance due to a large combinatorial search …
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 …
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
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 …
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
In this paper, we present accuracy-time efficient hyperparameter optimization (HPO) using
advantage actor-critic (A2C)-based reinforcement learning (RL) and early stopping in …
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 …
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 …
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 …
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 …
moniulotteisessa avaruudessa, voidaan käyttää useissa luonnollisen kielen tehtävissä hyvin …