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
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016.
Yuanjun Xiong, Limin Wang 외 8명
arXiv (Cornell University)
|
2016
|
132 회 인용
Deceiving Google's Perspective API Built for Detecting Toxic Comments
Hossein Hosseini, Sreeram Kannan 외 2명
arXiv (Cornell University)
|
2017
|
132 회 인용
Deep Contrast Learning for Salient Object Detection
Guanbin Li, Yizhou Yu
arXiv (Cornell University)
|
2016
|
132 회 인용
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos 외 4명
arXiv (Cornell University)
|
2019
|
132 회 인용
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely 외 2명
arXiv (Cornell University)
|
2018
|
132 회 인용
Evaluating Large Language Models in Theory of Mind Tasks
Michał Kosiński
arXiv (Cornell University)
|
2023
|
132 회 인용
Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation
Ling Wang, Tiago Luís 외 6명
arXiv (Cornell University)
|
2015
|
132 회 인용
Global Convergence of Online Limited Memory BFGS
Aryan Mokhtari, Alejandro Ribeiro
arXiv (Cornell University)
|
2014
|
132 회 인용
Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models
Quan Wang
arXiv (Cornell University)
|
2012
|
132 회 인용
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen, Daphna Weinshall
arXiv (Cornell University)
|
2019
|
132 회 인용
353
354
355
356
357
