Resources

Quotes

• "Don't pray for fair weather, pray for courage" - Mau Piailug (1932-2010) master non-instrument open-ocean navigator

• “Science is built up with facts, as a house is with stones.  But a collection of facts is no more a science than a heap of stones is a house” - Henri Poincare

Useful Links (2024)

• How to ML Rebuttal – A Brief Guide

  • if you are ever wondering on how to do rebuttals I recommend this amazing guide to rebuttals from @j_foerst and @_rockt  [link]

• Let's fix academic posters! 

• Advice on Applying to Grad School in Computer Science

• Network Science by Albert-László Barabási

• MetaPaper: Lessons from 1K rejected research papers by Christos Faloutsos

• Twitter Use and its Effects on Student Perception of Instructor Credibility

• Tips for Writing a Research Paper using LaTeX by Guanying Chen

Useful Links (2023)

• A Short Guide How To Write Your Bibliography by Arno Solin

Useful Links (2022)

• Awesome Knowledge Management: A curated list of amazingly awesome articles, people, applications, software libraries and projects related to the knowledge management space.

• Graph signal processing for machine learning [Slides1] [Slides2] [Slides3] 

  • IEEE ICASSP Tutorial, Toronto, ON, Canada, June 2021. 

• Advice for writing LaTeX documents by Diomidis Spinellis

• paper_writing_tips: Paper Writing Tips

• awesome-phd-advice: Collection of advice for prospective and current Ph.D. students

• the-art-of-command-line : Master the command line, in one page

• ResearchRabbit

• Connected Paper

• Classical ML Equations in LaTeX

• Machine Learning Notation

• LaTeX files for the Deep Learning book notation

• Handout for the tutorial "Creating publication-quality figures with matplotlib" 

• awesome-scholarly-productivity

• Tree style tabs

  • Code snippets for custom style

    • Hide horizontal tabs at the top of the window

• Cowboy: An Agile Programming Methodology for a Solo Programmer

• Most cited mathematicians

• p-values and statistical tests 

• coloring t-SNE

• How to Use t-SNE Effectively

• Drawing utilities for publication quality plots of networks 

• AI for Time Series (AI4TS) Papers, Tutorials, and Surveys

  • A professional list of Papers, Tutorials, and Surveys on AI for Time Series in top AI conferences and journals. 

• Transformers in Time-series

  • A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series. 

• IJCAI2022-Tutorial-Time-Series

  • IJCAI'22 Tutorial, Robust Time Series Analysis from Theory to Applications in the AI Era 

• Time-series Tutorial in KDD 2022 

  • Github source for KDD'22 Time Series Tutorial website 

• Report your environment! python script from pyTorch

• Statistical Comparisons of Classifiers over Multiple Data Sets (JMLR 2006)

• I Am Iron Man: Leadership Lessons From Tony Stark written by John Rampton

• Acceptance rates for the major AI conferences

• Graph Neural Networks Slides by Makoto Yamada, 2020

• Math behind GCN by Zhiping Xiao, 2018 [slide]

• How to write a good paper by William T. Freeman [slide]

• Multi-slice CT Image Reconstruction by Jiang Hsieh [slide]

• CT Image Reconstruction by Terry Peters [slide]

• Stanford EE369C: Medical Image Reconstruction [link]

• OpenResearch 

• atlas - ICLR 2023 submitted papers

• Generative AI: A Creative New World

• Diffusion/SDE/ODE-based generative models papers (ICLR2023 Submitted)

• Awesome-math: A curated list of awesome mathematics resources

• Awesome-tips: various tips for researchers

• How To Do A Good Literature Review | The PhD Voice Twitter

• Graph Neural Networks: Foundations, Frontiers, and Applications

• Geometric Deep Learning (Grids, Groups, Graphs, Geodesics, and Gauges)

• Novelty in Science | Perceiving Systems Blog (perceiving-systems.blog) 

Useful Links(2021)

Help my research! (from Advice to Students by Prof. Seo)

• How to get your paper accepted, written by Peter Pietzuch, Department of Computing Imperial College London

• How to read a CS research paper, written by Philip W. L. Fong

• Advice on research and writing

• Annotated Pytorch Paper Implementation

Accelerate your research!

• Connected Paper: this helps me to find the related work easily.

When you fell apart but got back up again

• Don't let jealousy own your work

For a Good Talk!

• David Patterson: "How to Give a Bad Talk"

• How to Give a Technical Presentation by Michael Ernst

Non-systematic Literature Review on Graphs (e.g. Awesome lists)

Graph Neural Networks

• Graph-based Deep Learning Literature: links to conference publications in graph-based deep learning

• Awesome Graph Neural Networks

• Must-read papers on GNN

• GNNs and related works list: A list for GNNs and related works.

• awesome deep gnn: Papers about developing deep Graph Neural Networks (GNNs)

• Awesome Semi-Supervised Learning: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

• Awesome Community Detection Research Papers: A curated list of community detection research papers with implementations.

• awesome-self-supervised-gnn: Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).

• Graph Theory Book

Traffic Forecasting

• GNN4Traffic: This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

• Traffic Prediction: A tabular summary of paper and publically available datasets. 

• Awesome Traffic Prediction: Useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories.

Dynamic Graphs

• Awesome-DynamicGraphLearning: Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs)

Further Reading

• Graph at ICLR 2021: The core reading list for GNN researchers in ICLR 2021

• Graph Adversarial Learning Literature: A curated list of adversarial attacks and defenses papers on graph-structured data.

• SNAP Graph Workshop 2021: Stanford Graph Learning Online Workshop

• Reading List of Papers about Homophily (Assortative) in GNNs

• Papers about developing DL methods on disassortative graphs

Non-systematic Literature Review (e.g. Awesome lists)

Recommender Systems 

• Awesome Recommender System: The collection of papers about the recommender system

Differential Equations 

• Latent Stochastic Differential Equations David Duvenaud | TMLS2019

Tabular Data

• A standard framework for modeling Deep Learning Models for tabular data

• Review on GANs for tabular data

Energy Based Models

• Collecting research materials on EBM/EBL (Energy Based Models, Energy Based Learning)

Software Engineering

• Awesome Software Engineering: A curated list of awesome software engineering resources.

Useful Links(2020)

• Common Errors in English Usage

• Writing Technical Articles

• How to Have a Bad Career in Research/Academia

  • David Patterson: "How to Have a Bad Career" | Talks at Google

• The 3 Skills That Helped Me Become a Better Software Engineer

• Research Portal for Computer Science 

• How to write a great research paper

  • Simon Peyton Jones: "How to write a great research paper" | Talks at  Microsoft Research

• Writing a great research proposal, Simon Peyton Jones

• How to give a great research talk, Simon Peyton Jones

• How to give a good research talk, Stephanie Weirich, University of Pennsylvania

• How Not to Present a Paper, Anders Møller, Aarhus University

Software Engineering

• The Fractal Geometry of Software Design - Vlad Khononov - DDD Europe 2022

• Software Engineering Academic Genealogy 

• Software Engineering Conferences

• The SIGSOFT Chronology: its Officers, Conferences, Awards, and more

• A Wiki for Call For Papers - Software Engineering

• The Five Laws of SE for AI