πŸ€— Paper Style Guide

Overview

πŸ€— Paper Style Guide

(Work in progress, send a PR!)

Libraries to Know

General

  • When in doubt use sections -> Introduction, Background, Model, Training, Methods, Results, Discussion, Conclusion.
  • Tables should always follow this guide
  • Tables / Figures should always float. Never inline in the text.
  • When using natbib, \citet is for when the citation is a noun, and \citep is for when it is at the end.
  • Captions should be short but fully self-explanatory of the columns / rows. They should not use 1st person.
  • Abstracts should be 1 paragraph. When in doubt -> Context, Problem, Idea 1, Idea 2, Results.
  • Section titles should be starting-caps.
  • The goal of related work is not just to list papers, but to explicitly make claims as to how your work differs from each one.
  • Figures should have a white background and large fonts. Do not screenshot! Generate a high-res, pdf output.
  • Use present tense (almost) everywhere.
  • You do not need a summary paragraph at the end of your intro.
  • All empirical results must be in a table or figure.
  • Methods section should not introduce new modeling. Enumerate the tasks, baselines, hyperparameters.
  • Results section should not introduce new tasks or models. Summarize the tables.
  • Any non-trivial notation should be introduced as early possible. Ideally background.
  • 8 pages is an extremely hard limit.
  • Always use `` '' for quotes not " ".
  • Use bold sparingly. Opt for italics for new technical terms.

Small Tips

  • Turn off \usepackage[review]{emnlp} to \usepackage[]{emnlp} while editing to fix overleaf linking.
  • Use \newcommand{\todo}[1]{{\small\color{red}{\bf [*** Todo: #1]}}} for inline comments.

Links

Exercises

  • What are the 3 contributions of the paper?
  • Do my experiments convincingly prove each of these are true?
  • Can I cut anything that does not satisfy these?
  • Would someone who has not read a paper in 2 years understand what is happening?
Owner
Hugging Face
The AI community building the future.
Hugging Face
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
Deep metric learning methods implemented in Chainer

Deep Metric Learning Implementation of several methods for deep metric learning in Chainer v4.2.0. Proxy-NCA: No Fuss Distance Metric Learning using P

ronekko 156 Nov 28, 2022
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

Friederike Metz 7 Apr 23, 2022
πŸ”₯ Cannlytics-powered artificial intelligence πŸ€–

Cannlytics AI πŸ”₯ Cannlytics-powered artificial intelligence πŸ€– πŸ—οΈ Installation πŸƒβ€β™€οΈ Quickstart 🧱 Development 🦾 Automation πŸ’Έ Support πŸ›οΈ License ?

Cannlytics 3 Nov 11, 2022
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP

CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP Andreas FΓΌrst* 1, Elisabeth Rumetshofer* 1, Viet Tran1, Hubert Ramsauer1, Fei Tang3, Joh

Institute for Machine Learning, Johannes Kepler University Linz 133 Jan 04, 2023
A curated list of awesome Machine Learning frameworks, libraries and software.

Awesome Machine Learning A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php. If you

Joseph Misiti 57.1k Jan 03, 2023
Black-Box-Tuning - Black-Box Tuning for Language-Model-as-a-Service

Black-Box-Tuning Source code for paper "Black-Box Tuning for Language-Model-as-a-Service". Being busy recently, the code in this repo and this tutoria

Tianxiang Sun 149 Jan 04, 2023
Image-Scaling Attacks and Defenses

Image-Scaling Attacks & Defenses This repository belongs to our publication: Erwin Quiring, David Klein, Daniel Arp, Martin Johns and Konrad Rieck. Ad

Erwin Quiring 163 Nov 21, 2022
Code for Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks Under construction. Description Code for Phase diagram of S

Rodrigo Veiga 3 Nov 24, 2022
Code for the paper "Balancing Training for Multilingual Neural Machine Translation, ACL 2020"

Balancing Training for Multilingual Neural Machine Translation Implementation of the paper Balancing Training for Multilingual Neural Machine Translat

Xinyi Wang 21 May 18, 2022
Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks

Assessing the Influence of Models on the Performance of Reinforcement Learning Algorithms applied on Continuous Control Tasks This is the master thesi

Giacomo Arcieri 1 Mar 21, 2022
Code accompanying paper: Meta-Learning to Improve Pre-Training

Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P

28 Dec 31, 2022
Finite difference solution of 2D Poisson equation. Can handle Dirichlet, Neumann and mixed boundary conditions.

Poisson-solver-2D Finite difference solution of 2D Poisson equation Current version can handle Dirichlet, Neumann, and mixed (combination of Dirichlet

Mohammad Asif Zaman 34 Dec 23, 2022
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking

Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking We revisit and address issues with Oxford 5k and Paris 6k image retrieval benchm

Filip Radenovic 188 Dec 17, 2022
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models

PipeTransformer: Automated Elastic Pipelining for Distributed Training of Large-scale Models This repository is the official implementation of the fol

DistributedML 41 Dec 06, 2022
Reimplementation of the paper "Attention, Learn to Solve Routing Problems!" in jax/flax.

JAX + Attention Learn To Solve Routing Problems Reinplementation of the paper Attention, Learn to Solve Routing Problems! using Jax and Flax. Fully su

Gabriela Surita 7 Dec 01, 2022
(ICCV 2021) ProHMR - Probabilistic Modeling for Human Mesh Recovery

ProHMR - Probabilistic Modeling for Human Mesh Recovery Code repository for the paper: Probabilistic Modeling for Human Mesh Recovery Nikos Kolotouros

Nikos Kolotouros 209 Dec 13, 2022
Code for the paper "Next Generation Reservoir Computing"

Next Generation Reservoir Computing This is the code for the results and figures in our paper "Next Generation Reservoir Computing". They are written

OSU QuantInfo Lab 105 Dec 20, 2022
Vision-Language Pre-training for Image Captioning and Question Answering

VLP This repo hosts the source code for our AAAI2020 work Vision-Language Pre-training (VLP). We have released the pre-trained model on Conceptual Cap

Luowei Zhou 373 Jan 03, 2023