Learning-Deep-Learning

Paper notes

This repository contains my paper reading notes on deep learning and machine learning. It is inspired by Denny Britz and Daniel Takeshi. A minimalistic webpage generated with Github io can be found here.

About me

My name is Patrick Langechuan Liu. After about a decade of education and research in physics, I found my passion in deep learning and autonomous driving. Currently I am leading the development of perception features at Xpeng Motors, a fast growing autonomous driving company.

What to read

If you are new to deep learning in computer vision and don’t know where to start, I suggest you spend your first month or so dive deep into this list of papers. I did so (see my notes) and it served me well.

Here is a list of trustworthy sources of papers in case I ran out of papers to read.

My review posts by topics

I regularly update my blog in Toward Data Science.

2024-03 (2)

2024-02 (7)

2023-12 (4)

2023-09 (3)

2023-08 (3)

2023-07 (6)

2023-06 (5)

2023-05 (7)

2023-04 (1)

2023-03 (5)

2023-02 (4)

2023-01 (2)

2022-11 (1)

2022-10 (1)

2022-09 (3)

2022-08 (1)

2022-07 (8)

2022-06 (3)

2022-03 (1)

2022-02 (1)

2022-01 (1)

2021-12 (5)

2021-11 (4)

2021-10 (3)

2021-09 (11)

2021-08 (11)

2021-07 (1)

2021-06 (2)

2021-04 (5)

2021-03 (4)

2021-01 (7)

2020-12 (17)

2020-11 (18)

2020-10 (14)

2020-09 (15)

2020-08 (26)

2020-07 (25)

2020-06 (20)

2020-05 (19)

2020-04 (14)

2020-03 (15)

2020-02 (12)

2020-01 (19)

2019-12 (12)

2019-11 (20)

2019-10 (18)

2019-09 (17)

2019-08 (18)

2019-07 (19)

2019-06 (12)

2019-05 (18)

2019-04 (12)

2019-03 (19)

2019-02 (9)

2019-01 (10)

2018

2017 and before

Papers to Read

Here is the list of papers waiting to be read.

Deep Learning in general

Self-training

2D Object Detection and Segmentation

Fisheye

Video Understanding

Pruning and Compression

Architecture Improvements

Reinforcement Learning

3D Perception

Stereo and Flow

Traffic light and traffic sign

Datasets and Surveys

Unsupervised depth estimation

Indoor Depth

lidar

Egocentric bbox prediction

Lane Detection

Tracking

keypoints: pose and face

General DL

Mono3D

Radar Perception

SLAM

Radar Perception

Reviews and Surveys

Beyond Perception in Autonomous Driving

Prediction and Planning

Annotation and Tooling

Low level DL

Early NLP papers

Non-DL

Technical Debt

To be organized (CVPR 2021 and ICCV 2021 the pile to be read)

TODO