Computer Science papers

Pofolia organises Computer Science into 11 subfields: 1069 papers in total (1958–2026), each with a short summary. The subfields come first, then the most-cited work across the whole field.

Subfields

• Computer Networks and Communications 102 papers
• Computer Science Applications 105 papers
• Computer Graphics and Computer-Aided Design 87 papers
• Computer Vision and Pattern Recognition 99 papers
• Information Systems 100 papers
• Hardware and Architecture 97 papers
• Computational Theory and Mathematics 91 papers
• Human-Computer Interaction 78 papers
• Signal Processing 86 papers
• Artificial Intelligence 127 papers
• Software 97 papers

At a glance

• Most cited: Deep Residual Learning for Image Recognition (2016, 223,583 citations).
• The 12 papers listed have 928,974 citations between them.
• 3 of the 3 with a known journal quartile appeared in a Q1 journal.
• 6 have a free full text (open access).
• Most frequent journals: arXiv (Cornell University), DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), Communications of the ACM.
• Published between 2012 and 2023.

Most cited across the field

Ranked by citation count. Because citations accumulate over time, this list naturally leans towards work published a few years ago; for where the field is now, see “recently added”. How to read the signals

• Deep Residual Learning for Image Recognition

  2016 · 223,583 citations

  A new residual learning framework enables training significantly deeper neural networks, achieving state-of-the-art image recognition performance.

  Go to source →

• Fitting Linear Mixed-Effects Models Using lme4

  Journal of Statistical Software · 2015 · Q1 · SJR 3.00 · FWCI 6077.98 · 86,393 citations · Open access

  The lme4 package in R provides the `lmer` function to fit linear mixed-effects models using maximum likelihood or REML estimation.

  Go to source →

• Adam: A Method for Stochastic Optimization

  UvA-DARE (University of Amsterdam) · 2014 · FWCI 1726.23 · 84,793 citations

  Adam is a new, computationally efficient algorithm for gradient-based optimization of stochastic objective functions, using adaptive estimates of lower-order moments.

  Go to source →

• Inline Hardware KV-Cache Compression for Long-Context Transformer Inference: An Architectural Case for a Memory-Path Compression Engine

  DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · FWCI 7347.72 · 77,000 citations · Open access

  This paper presents an AI/TP system that automatically proves a significant portion of Mizar theorems, achieving 60% in a hammer setting and 75% when guided by human-selected premises.

  Go to source →

• ImageNet classification with deep convolutional neural networks

  Communications of the ACM · 2017 · Q1 · SJR 1.00 · FWCI 4435.36 · 75,715 citations · Open access

  A deep convolutional neural network achieved state-of-the-art ImageNet classification, reducing top-5 error rates to 17.0% in 2010 and a winning 15.3% in 2012.

  Go to source →

• Very Deep Convolutional Networks for Large-Scale Image Recognition

  arXiv (Cornell University) · 2014 · 75,542 citations · Open access

  Increasing convolutional network depth to 16-19 weight layers, using small 3x3 filters, significantly improves accuracy in large-scale image recognition over prior configurations.

  Go to source →

• Scikit-learn: Machine Learning in Python

  arXiv (Cornell University) · 2012 · 63,878 citations · Open access

  Scikit-learn is a new Python module that integrates a wide range of machine learning algorithms for medium-scale problems, designed for non-specialists.

  Go to source →

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

  IEEE Transactions on Pattern Analysis and Machine Intelligence · 2016 · Q1 · SJR 4.00 · FWCI 1672.08 · 54,589 citations

  This paper introduces a Region Proposal Network (RPN) that integrates seamlessly with object detection networks, enabling real-time performance by generating region proposals at nearly no computational cost.

  Go to source →

• XGBoost

  2016 · 49,653 citations · Open access

  XGBoost is a novel, scalable tree boosting system designed for high performance on large datasets, achieving state-of-the-art results in machine learning challenges.

  Go to source →

• Going deeper with convolutions

  2015 · 46,948 citations

  A novel deep convolutional neural network architecture, Inception, has set a new state-of-the-art for image classification and detection on the ILSVRC14 challenge.

  Go to source →

• AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

  DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2018 · FWCI 2503.48 · 45,742 citations

  A new framework addresses the challenge of answering qualitative spatial questions by integrating a geoparser, a crisp reasoning system, and answer extraction.

  Go to source →

• Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach

  2013 · FWCI 1860.33 · 45,138 citations

  This book introduces mediation, moderation, and conditional process analysis, presenting a unified regression-based framework for understanding complex relationships in data.

  Go to source →

Journals in this field

The journals that publish most of this field’s papers. Quartile (Q1–Q4), SJR and h-index are from SCImago Journal Rank; “in this field” is how many of the journal’s pooled papers belong here. What is a Q1 journal? · What is the h-index?

← All fields