Artificial Intelligence papers
Pofolia’s corpus holds 127 papers from Artificial Intelligence (1958–2026), each with a short summary. Below, the 20 most-cited, then the most recently added.
At a glance
- Most cited: Fitting Linear Mixed-Effects Models Using lme4 (2015, 86,393 citations).
- The 20 papers listed have 727,955 citations between them.
- 4 of the 4 with a known journal quartile appeared in a Q1 journal.
- 10 have a free full text (open access).
- Most frequent journals: arXiv (Cornell University), DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), Advances in Engineering Software.
- Published between 2012 and 2025.
Most cited
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
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.
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.
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.
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.
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.
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.
SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · Q1 · SJR 15.00 · FWCI 2401.92 · 38,420 citations
SciPy 1.0 marks a significant milestone for the open-source Python library, solidifying its role as a de facto standard for scientific computing with over a decade of development and widespread adoption.
Dropout: a simple way to prevent neural networks from overfitting
2014 · FWCI 1844.83 · 34,283 citations
A new technique called 'dropout' randomly removes units from neural networks during training, preventing them from becoming too specialized and significantly reducing overfitting.
Glove: Global Vectors for Word Representation
2014 · 33,863 citations
A new global log-bilinear regression model, Glove, efficiently learns word vector representations by training on a word-word co-occurrence matrix, achieving 75% accuracy on a word analogy task.
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
2014 · 24,614 citations · Open access
This paper introduces an RNN encoder-decoder model that learns to represent phrases and translate them, outperforming phrase-based statistical machine translation (SMT) on a French-to-English task.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
arXiv (Cornell University) · 2015 · 24,393 citations · Open access
Batch Normalization, a new technique that normalizes layer inputs within the model architecture, significantly accelerates deep neural network training by reducing internal covariate shift.
Array programming with NumPy
Nature · 2020 · Q1 · SJR 19.00 · FWCI 1249.42 · 22,658 citations · Open access
NumPy is the core Python library enabling powerful array programming for scientific data analysis across diverse fields like physics, astronomy, and finance.
Xception: Deep Learning with Depthwise Separable Convolutions
2017 · 19,087 citations
A new deep convolutional neural network architecture, Xception, replaces Inception modules with depthwise separable convolutions, achieving superior performance on large-scale image classification tasks.
Array programming with NumPy
TUScholarShare (Temple University) · 2020 · FWCI 267.66 · 18,809 citations · Open access
NumPy is the core Python library enabling powerful, compact, and expressive array programming for scientific data analysis across diverse fields.
Grey Wolf Optimizer
Advances in Engineering Software · 2014 · Q1 · SJR 1.00 · FWCI 352.66 · 18,589 citations
A new meta-heuristic algorithm, the Grey Wolf Optimizer (GWO), is introduced, mimicking the social hierarchy and hunting behaviors of grey wolves.
Efficient Estimation of Word Representations in Vector Space
arXiv (Cornell University) · 2013 · 18,154 citations · Open access
This paper introduces two new model architectures that significantly improve the accuracy and reduce the computational cost of creating word vector representations from massive datasets.
Distributed Representations of Words and Phrases and their Compositionality
arXiv (Cornell University) · 2013 · 18,083 citations · Open access
This paper introduces extensions to the Skip-gram model for learning word vector representations, significantly improving training speed and vector quality through subsampling frequent words and a new negative sampling method.
HISTORIAE, History of Socio-Cultural Transformation as Linguistic Data Science. A Humanities Use Case
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2019 · FWCI 1423.45 · 17,374 citations
This paper introduces a novel method for automatically learning problem instance features directly from high-level representations using a transformer encoder, improving the selection of optimal model-solver combinations for combinatorial optimization.
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
Dagstuhl Research Online Publication Server · 2025 · 16,189 citations
A new approach, APISS, effectively detects functionality-specific software vulnerabilities by retrieving equivalent APIs using API documentation and signatures, achieving 0.81 Top-1 Accuracy compared to baselines' 0.55.
Density Estimation for Statistics and Data Analysis
2018 · FWCI 329.90 · 15,980 citations
This book aims to bridge the gap between technical density estimation research and its practical applications in statistics, making the subject more accessible.
Recently added
DOTA-ME-CS: daily oriented text audio-Mandarin English-Code switching dataset
Journal of Ambient Intelligence and Humanized Computing · 2026 · Q2 · FWCI 5.83 · 1 citations
A new Mandarin-English code-switching speech dataset, DOTA-ME-CS, has been released, featuring 18.54 hours of audio from 34 participants, enhanced with AI techniques for diversity.
MOUFLON: multi-group modularity-based fairness-aware community detection
Data Mining and Knowledge Discovery · 2026 · Q1 · SJR 1.00 · FWCI 5.83 · 1 citations · Open access
MOUFLON, a new modularity-based community detection method, introduces a proportional balance fairness metric that allows tuning the trade-off between partition quality and fairness across multi-group and imbalanced networks.
Disorder-promoted stability
Science · 2026 · Q1 · SJR 10.00 · FWCI 5.83 · 1 citations
Nodal heterogeneity can enhance network stability, even with random disorder, when nodal dynamics are higher-dimensional and yield non-Hermitian Jacobians, challenging prior assumptions.
Training Machine Learning Models at the Edge: A Survey
ACM Transactions on Intelligent Systems and Technology · 2026 · Q1 · SJR 2.00 · FWCI 5.83 · 8 citations · Open access
This survey identifies federated learning as the dominant approach for optimizing Machine Learning (ML) model training at the edge, synthesizing existing knowledge and highlighting future trends.
Fermion-to-qubit encodings with arbitrary code distance
Quantum Science and Technology · 2026 · Q1 · SJR 1.00 · FWCI 5.83 · 1 citations · Open access
A new framework enables arbitrary scaling of code distance for local fermion-to-qubit encodings in 1D and 2D without increasing stabilizer weights, by embedding low-distance encodings as topological defects within the surface code.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
ACM Computing Surveys · 2026 · Q1 · SJR 5.00 · FWCI 23.33 · 40 citations
This survey systematically reviews techniques for making Large Language Models (LLMs) more efficient across computational, memory, energy, financial, and network resources.
Agent READMEs: An Empirical Study of Context Files for Agentic Coding
ACM Transactions on Software Engineering and Methodology · 2026 · Q1 · SJR 1.00 · FWCI 11.67 · 2 citations · Open access
Agent context files (e.g., AGENTS.md) are complex, evolving artifacts, not static docs, with developers prioritizing functional context (e.g., test procedures 75.9%) over non-functional requirements like security (14.8%).
Omnidirectional type inference for ML: principality any way
ACM Transactions on Programming Languages and Systems · 2026 · Q2 · FWCI 5.95 · 1 citations · Open access
A new omnidirectional type inference algorithm restores principality (unique most general type) to ML-like languages extended with fragile features like static overloading and first-class polymorphism, by allowing type information to flow dynamically rather than in a fixed order.
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?
| Journal | Quartile | SJR | h-index | In this field | Summaries |
|---|---|---|---|---|---|
| ACM Computing Surveys | Q1 | 5.00 | 260 | 4 | 17 |
| Nature | Q1 | 19.00 | 1495 | 3 | 48 |
| Journal of the American Statistical Association | Q1 | 3.00 | 238 | 3 | 7 |
| Science | Q1 | 10.00 | 1433 | 2 | 268 |
| Quantum | Q1 | 2.00 | 80 | 2 | 7 |
| ACM Transactions on Software Engineering and Methodology | Q1 | 1.00 | 95 | 2 | 7 |
| Nature Medicine | Q1 | 17.00 | 676 | 1 | 11 |
| Management Science | Q1 | 6.00 | 321 | 1 | 10 |
Every journal in Artificial Intelligence (Q1–Q4) →
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- Computer Networks and Communications
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- Computer Vision and Pattern Recognition
- Information Systems
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- Computational Theory and Mathematics
- Human-Computer Interaction
- Signal Processing
- Software