For my own research and teaching, I follow AI papers. Here is a list of papers I find interesting. I analysed 3 lists (links below) as meta-references for lists of good papers

From that list and also my own interests, here is a set of papers I find interesting in 2020

CORE PAPERS

These influence how AI algorithms could develop in future

1) ADA: Training Generative Adversarial Networks with Limited Data

https://arxiv.org/abs/2006.06676

A method for training a GAN using a very small number of images. Developed by Nvidia.

2) Language Models are Few-Shot Learners

https://arxiv.org/abs/2005.14165

This is the main GPT-3 paper. I have covered before why GPT-3 is disruptive before – Could GPT-3 Change The Way Future AI Models Are Developed and Deplo…

3) Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

https://arxiv.org/abs/2005.04118

Checkist presents a task agnostic way of testing NLP models. Cheklist demonstrates that measures beyond accuracy need to be considered for evaluating some NLP tasks.

4) AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

https://arxiv.org/abs/2003.03384

A paper from Google which aims to show that AutoML can automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks.

5) Towards a Human-like Open-Domain Chatbot

https://arxiv.org/abs/2001.09977

The Meena chatbot from Google can chat about virtually anything in contrast to other chatbots which are more specialized.

INTERESTING PAPERS

1) YOLOv4: Optimal Speed and Accuracy of Object Detection

https://arxiv.org/abs/2004.10934

Improves the YOLO algorithm further in terms of accuracy. AI algorithms continue to improve – even incrementally – but over time all these small research improvements make the technology mainstream. This was a similar trajectory followed by language translation.

2) Unsupervised Translation of Programming Languages

https://arxiv.org/abs/2006.03511

Converts code from a programming language to another without any supervision – for example python functions can be translated to C++ functions.

3) High-Resolution Neural Face Swapping for Visual Effects

https://studios.disneyresearch.com/2020/06/29/high-resolution-neura…

This paper from Disney research got a lot of traction. The goal of the paper is to swap the face of a target actor from a source actor while maintaining the actor’s behaviour and performance (for example when ageing the actor).

4) Learning to Cartoonize Using White-box Cartoon Representations

https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Learnin…

AI can cartoonize any picture or video you feed it in a specified cartoon style.

5) Reconstruct Photorealistic Scenes from Tourists’ Public Photos on the Internet

https://medium.com/towards-artificial-intelligence/reconstruct-phot…

Using tourists’ public photos from the internet, reconstruct viewpoints of a scene

6) A New Brain-inspired Intelligent System Drives a Car Using Only 19 Control Neurons

https://morioh.com/p/3bf382d50276

AI based on brains of tiny animals (like threadworms – then used to perform complex functions like controlling a self-driving car.

7) DeOldify

https://github.com/jantic/DeOldify

A technique to colorize and restore old black and white images

8) Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

https://arxiv.org/abs/2003.11927

A significantly-improved data-driven global weather forecasting framework using a deep convolutional neural network (CNN) to forecast several basic atmospheric variables on a global grid.

9) A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning,

https://ojs.aaai.org//index.php/AAAI/article/view/5376

A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning based on a new stacking ensemble method