Research Papers Wiki

A comprehensive offline knowledge base synthesizing ~240 research papers across neuroscience, deep learning, medical AI, generative modeling, and more. Each topic page provides key ideas, breakthrough findings, and connections to related fields.

⚠️ Warning: Wiki generated by LLM from curated documents. Double check the synthesis.

⚠️ Warning: Download links might be wrong but you can generally find sources by searching for paper title + author + publication year. All papers are open access.

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Table of Contents

I. Foundational Deep Learning

• Convolutional Networks & Image Recognition
• Transformers & Attention Mechanisms
• Residual Learning & Efficient Architectures
• Neural Implicit Representations (NeRF, SIREN)

II. Generative Models & Density Estimation

• Variational Autoencoders (VAEs)
• Diffusion Models
• Normalizing Flows
• GFlowNets & Combinatorial Generation
• Energy-Based Models & OOD Detection

III. Bayesian Inference, Uncertainty & Probabilistic ML

• Bayesian Foundations
• Variational Inference Methods
• Uncertainty Quantification in Deep Learning
• Conformal Prediction & Calibration

IV. Graph Neural Networks & Relational Learning

• GNN Foundations & Expressiveness
• GNNs for Time Series & Dynamical Systems
• GNNs in Biology & Neuroscience

V. Physics-Informed ML & Scientific Computing

• Physics-Informed Neural Networks (PINNs)
• Neural Operators & PDE Solvers
• Neural Differential Equations
• Data-Driven Dynamical Systems

VI. Neuroimaging: Acquisition, Preprocessing & Pipelines

• MRI Physics & Data Formats
• FreeSurfer & Cortical Surface Analysis
• SPM & Statistical Neuroimaging
• Deep Learning Neuroimaging Pipelines

VII. Brain Segmentation & Lesion Detection

• Whole-Brain Segmentation
• Brain Tumor Detection & Segmentation
• Stroke & Ischemic Lesion Segmentation
• Anomaly Detection in Brain Imaging

VIII. Normative Modeling & Brain Morphometry

• Normative Modeling Frameworks
• Applications: Alzheimer's, Schizophrenia, ADHD
• Brain Morphometry & Cortical Complexity

IX. Neurodegenerative Disease & Alzheimer's Research

• Molecular Mechanisms: Prions, Amyloids, Mitochondria
• Biomarker Staging & Diagnosis
• Computational Brain Simulation

X. Medical Image Analysis & Clinical AI

• Deep Learning for Medical Imaging (Reviews)
• Domain Generalization & Transfer Learning
• Cancer Symptom Prediction & Pharmacology

XI. Federated Learning & Privacy-Preserving ML

• Federated Learning Frameworks
• Secure Aggregation & SMPC
• Privacy Attacks & Defenses
• Blockchain-Based Federated Learning

XII. Large Language Models & Reasoning

• LLM Architectures
• Chain-of-Thought & Prompting
• AI Safety & Adversarial Attacks
• Multimodal Foundation Models

XIII. Immunology, mRNA & Molecular Biology

• Immune Response & Vaccines
• RNA Biology & Therapeutics
• Protein Structure & Drug Discovery
• Nanotechnology & Biosynthesis

XIV. Optimization, RL & Scientific Discovery

• Reinforcement Learning
• Combinatorial Optimization
• Symbolic Regression & Interpretable ML
• AI for Scientific Discovery

XV. Distributed Systems & Infrastructure

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Cross-Topic Intersections

• Deep Learning × Neuroimaging — How transformers, diffusion models, and normative modeling reshape brain science
• Generative Models × Drug Discovery & Molecular Design — From VAEs to GFlowNets for protein folding and chemical design
• Uncertainty Quantification × Clinical Decision-Making — Bayesian methods, conformal prediction, and trustworthy medical AI
• Federated Learning × Healthcare — Privacy-preserving distributed learning for medical data
• Physics-Informed ML × Brain Modeling — Neural ODEs, PINNs, and computational neuroscience
• Sparse Models × Interpretability × Generalization — How sparsity and symbolic methods drive robust, explainable, and generalizable AI

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Generated April 2026.