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https://doi.org/10.1145/3316774
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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1038/nature21056openalex-snapshot
Dermatologist-level classification of skin cancer with deep neural networks
inferredcites →https://doi.org/10.1103/physrevlett.98.150201openalex-snapshot
Statistics of Critical Points of Gaussian Fields on Large-Dimensional Spaces
inferredcites →https://doi.org/10.1109/cvpr.2018.00611openalex-snapshot
Neural Motifs: Scene Graph Parsing with Global Context
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot
Learning Deconvolution Network for Semantic Segmentation
inferredcites →https://doi.org/10.1126/science.146.3642.347openalex-snapshot
Strong Inference
inferredcites →https://doi.org/10.1145/1045339.1045340openalex-snapshot
Artificial intelligence meets natural stupidity
inferredcites →https://doi.org/10.1145/1645953.1646031openalex-snapshot
Improvements that don't add up
inferredcites →https://doi.org/10.1145/3233231openalex-snapshot
The Mythos of Model Interpretability
inferredcites →https://doi.org/10.1257/aer.p20151066openalex-snapshot
Mathiness in the Theory of Economic Growth
inferredcites →https://doi.org/10.1609/aaai.v32i1.11694openalex-snapshot
Deep Reinforcement Learning That Matters
inferredcites →https://doi.org/10.1609/aimag.v9i4.952openalex-snapshot
How evaluation guides AI research
inferredcites →https://doi.org/10.18653/v1/n18-2085openalex-snapshot
Are All Languages Equally Hard to Language-Model?
inferredcites →https://doi.org/10.18653/v1/p16-1223openalex-snapshot
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
inferredcites →https://doi.org/10.2307/27757473openalex-snapshot
N-Rays: An Episode in the History and Psychology of Science
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1406.2572openalex-snapshot
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
inferredcites →https://doi.org/10.48550/arxiv.1412.0233openalex-snapshot
The Loss Surfaces of Multilayer Networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6544openalex-snapshot
Qualitatively characterizing neural network optimization problems
inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
inferredcites →https://doi.org/10.48550/arxiv.1502.06665openalex-snapshot
Reified Context Models
inferredcites →https://doi.org/10.48550/arxiv.1610.03483openalex-snapshot
Learning in Implicit Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1706.03691openalex-snapshot
Certified Defenses for Data Poisoning Attacks
inferredcites →https://doi.org/10.48550/arxiv.1711.07076openalex-snapshot
Does mitigating ML's impact disparity require treatment disparity?
inferredcites →https://doi.org/10.48550/arxiv.1904.09237openalex-snapshot
On the Convergence of Adam and Beyond

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4892176openalex-extracted
Strategic Overhaul: Reframing Data Integration for Optimal AI Utilization
inferred← citeshttps://doi.org/10.1007/978-3-030-76423-4_1openalex-snapshot
ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility
inferred← citeshttps://doi.org/10.1007/978-3-031-69300-7_21openalex-snapshot
Abduction in Real World Navigation. The Issue of Proxies in Recommender Systems
inferred← citeshttps://doi.org/10.1007/978-3-032-03028-3_8openalex-snapshot
Evidence Accumulation as a Combining Rule for Decision Forests
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104510openalex-snapshot
Network intrusion datasets: A survey, limitations, and recommendations
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128934openalex-snapshot
Virtual sample generation for small sample learning: A survey, recent developments and future prospects
inferred← citeshttps://doi.org/10.1038/s41598-022-19608-4openalex-snapshot
ProteinGLUE multi-task benchmark suite for self-supervised protein modeling
inferred← citeshttps://doi.org/10.1109/aann71145.2026.11709151openalex-snapshot
Neural Networks as Diagnostic Instruments: A Strong-Inference Methodology for Engineering Validation
inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00917openalex-snapshot
The Affective Growth of Computer Vision
inferred← citeshttps://doi.org/10.1109/icass69550.2026.11547503openalex-snapshot
Performance Comparison of Machine Learning Algorithms for Depression Risk Prediction using PHQ-9 Questionnaire
inferred← citeshttps://doi.org/10.1109/saner50967.2021.00061openalex-snapshot
MSR4ML: Reconstructing Artifact Traceability in Machine Learning Repositories
inferred← citeshttps://doi.org/10.1136/bmjopen-2021-059000openalex-snapshot
Deep learning for automatic brain tumour segmentation on MRI: evaluation of recommended reporting criteria via a reproduction and replication study
inferred← citeshttps://doi.org/10.1145/3453146openalex-snapshot
Lost in Transduction: Transductive Transfer Learning in Text Classification
inferred← citeshttps://doi.org/10.1145/3461702.3462521openalex-snapshot
Fair Machine Learning Under Partial Compliance
inferred← citeshttps://doi.org/10.1145/3514094.3534196openalex-snapshot
The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning
inferred← citeshttps://doi.org/10.1609/aimag.v42i3.18145openalex-snapshot
Progress in recommender systems research: Crisis? What crisis?
inferred← citeshttps://doi.org/10.18653/v1/2021.emnlp-main.153openalex-snapshot
Modeling Disclosive Transparency in NLP Application Descriptions
inferred← citeshttps://doi.org/10.2139/ssrn.5343363openalex-snapshot
Quantum Resonance at the Innovation Edge: How Outsider Synthesis of Physics and Ai Unlocked New Pathways to Machine Creativity
inferred← citeshttps://doi.org/10.3389/frai.2022.789076openalex-snapshot
The Issue of Proxies and Choice Architectures. Why EU Law Matters for Recommender Systems
inferred← citeshttps://doi.org/10.48550/arxiv.2012.08387openalex-snapshot
Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering
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