Skip to main content
누빈트
에이전트
더 알아보기
요금제
회사 소개
KO
Home
논문 리뷰
Computer Science
Computer Science 논문 리뷰
Computer Science 분야 주요 연구 논문을 연구 동기·방법·결과로 구조화한 AI 논문 리뷰 목록입니다.
전체
Computer Science
Mathematics
Physics and Astronomy
Engineering
Materials Science
Chemistry
Chemical Engineering
Earth and Planetary Sciences
Environmental Science
Energy
Biochemistry, Genetics and Molecular Biology
Neuroscience
Immunology and Microbiology
Agricultural and Biological Sciences
Medicine
Pharmacology, Toxicology and Pharmaceutics
Health Professions
Economics, Econometrics and Finance
Business, Management and Accounting
Decision Sciences
Social Sciences
Psychology
Arts and Humanities
필터
9999+개의 결과
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
Yixiao Ge, Feng Zhu 외 3명
arXiv (Cornell University)
|
2020
|
342 회 인용
SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Zirui Wang, Jiahui Yu 외 4명
arXiv (Cornell University)
|
2021
|
342 회 인용
A generic framework for privacy preserving deep learning
Théo Ryffel, Andrew Trask 외 5명
arXiv (Cornell University)
|
2018
|
341 회 인용
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks
Edward Choi, Siddharth Biswal 외 4명
arXiv (Cornell University)
|
2017
|
341 회 인용
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
Zehao Huang, Naiyan Wang
arXiv (Cornell University)
|
2017
|
341 회 인용
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh 외 3명
arXiv (Cornell University)
|
2021
|
341 회 인용
Florence: A New Foundation Model for Computer Vision
Lu Yuan, Dongdong Chen 외 21명
arXiv (Cornell University)
|
2021
|
340 회 인용
Hello Edge: Keyword Spotting on Microcontrollers
Yundong Zhang, Naveen Suda 외 2명
arXiv (Cornell University)
|
2017
|
340 회 인용
Hyperparameter Search in Machine Learning
Marc Claesen, Bart De Moor
arXiv (Cornell University)
|
2015
|
340 회 인용
Multi-Task Learning as Multi-Objective Optimization
Ozan Şener
arXiv (Cornell University)
|
2018
|
340 회 인용
120
121
122
123
124
