Here are Papers We Reviewed. We are science curious.

0Basics

Recent Readings for Basic Topics of Deep Neural Networks (since 2017) (Index of Posts):

No. Read Date Title and Information We Read @
1 2021, Jan, 3 Introductory reads on DeepLearning 2021-W0
2 2019, Nov, 3 A general survey 2019-fall Course
3 2019, Jan, 25 GNN Basics I - Deep Learning Advances on Graphs 2019-W1
4 2018, Feb, 20 Survey18- My Survey Talk at UVA HMI Seminar - A quick and rough overview of DNN 2018-me
5 2017, Aug, 31 Generative I - GAN tutorial by Ian Goodfellow 2017-W2
6 2017, Aug, 29 Reinforcement I - Pineau - RL Basic Concepts 2017-W2
7 2017, Aug, 22 Basic17 -Andrew Ng - Nuts and Bolts of Applying Deep Learning 2017-W1
8 2017, Jan, 20 Basic16- DNN to be Scalable 2017-team
9 2017, Jan, 19 Basic16- Basic Deep NN and Robustness 2017-team
10 2017, Jan, 18 Basic16- Basic Deep NN with Memory 2017-team
11 2017, Jan, 12 Basic16- Basic DNN Embedding we read for Ranking/QA 2017-team
12 2017, Jan, 12 Basic16- Basic DNN Reads we finished for NLP/Text 2017-team


Here is a detailed list of posts!


[1]: Introductory reads on DeepLearning

read on: - 03 Jan 2021
tutorial
Type Papers Paper URL Our Slides
Dr Qi Survey of 10 DeepLearning (DL) trends different from classic machine learning   OurSlide
Youtube Generative DL Basics Youtube1 + Youtube2 NA
Youtube Computation Graph for DL (pytorch vs. tensorflow Youtube URL + Youtube2 NA
Youtube Auto Differentiation for DL Youtube1+ Youtube2 NA
Youtube RL basics and DL-RL basics Youtube1 + Youtube2 NA
Youtube Probabilistic programming and in DL Pyro Youtube1 + Youtube2 NA
Youtube Basics of Software Testing for DL Youtube URL NA
Course Bill_CNN_Ng_Lecture_Notes   Bill’s Notes
Course Bill_caltechMLnotes_ALL   Bill’s Notes
classic Paper The Lottery Ticket Hypothesis   Morris’ Notes
classic Paper NLP From Scratch   Morris’ Notes
classic Paper Statistical Modeling The Two Cultures   Morris’ Notes
classic Paper Attention_is_All_You_Need   Eli’ Notes
classic Paper YOLO   Eli’ Notes
classic Paper Neural Turing Machine   Jake Survey
classic Paper BERT (Bidirectional Encoder Representation for Transformers): Pretraining of Deep Bidirectional Transformers for Language Understanding   Rishab Survey

[2]: A general survey

read on: - 03 Nov 2019
tutorial
Presenter Papers Paper URL Our Slides
Dr Qi Survey of Recent DeepLearning to 12 Groups / PDF    

[3]: GNN Basics I - Deep Learning Advances on Graphs

read on: - 25 Jan 2019
invariant scalable embedding
Presenter Papers Paper URL Our Notes
Basics GraphSAGE: Large-scale Graph Representation Learning by Jure Leskovec Stanford University URL + PDF  
Basics Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering by Xavier Bresson URL + PDF Ryan Pdf
Basics Gated Graph Sequence Neural Networks by Microsoft Research URL + PDF Faizan Pdf
Basics DeepWalk - Turning Graphs into Features via Network Embeddings URL + PDF  
Basics Spectral Networks and Locally Connected Networks on Graphs 1 Pdf GaoJi slides + Bill Pdf
Basics A Comprehensive Survey on Graph Neural Networks/ Graph Neural Networks: A Review of Methods and Applications Pdf Jack Pdf
GCN Semi-Supervised Classification with Graph Convolutional Networks Pdf Jack Pdf

[4]: Survey18- My Survey Talk at UVA HMI Seminar - A quick and rough overview of DNN

read on: - 20 Feb 2018
Presenter Papers Paper URL Our Slides
Dr. Qi A quick and rough survey of Deep-Neural-Networks   PDF

[5]: Generative I - GAN tutorial by Ian Goodfellow

read on: - 31 Aug 2017
generative GAN
Presenter Papers Paper URL Our Slides
NIPS 2016 ganerative adversarial network tutorial (NIPS 2016) paper + video + code  
DLSS 2017 Generative Models I - DLSS 2017 slideraw + video + slide  

[6]: Reinforcement I - Pineau - RL Basic Concepts

read on: - 29 Aug 2017
RL

Pineau - RL Basic Concepts

Presenter Papers Paper URL Our Slides
DLSS16 video    
RLSS17 slideRaw + video+ slide    

[7]: Basic17 -Andrew Ng - Nuts and Bolts of Applying Deep Learning

read on: - 22 Aug 2017
bias-variance
Presenter Papers Paper URL Our Slides
NIPS16 Andrew Ng - Nuts and Bolts of Applying Deep Learning: 1 video    
DLSS17 Doina Precup - Machine Learning - Bayesian Views (56:50m to 1:04:45 slides) video + slide    

[8]: Basic16- DNN to be Scalable

read on: - 20 Jan 2017
scalable random sparsity binary hash compression low-rank distributed dimension reduction pruning sketch Parallel
Presenter Papers Paper URL Our Slides
scalable Sanjiv Kumar (Columbia EECS 6898), Lecture: Introduction to large-scale machine learning 2010 [^1] PDF  
data scalable Alex Smola - Berkeley SML: Scalable Machine Learning: Syllabus 2012 [^2] PDF 2014 + PDF  
Binary Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1    
Model Binary embeddings with structured hashed projections 1 PDF PDF
Model Deep Compression: Compressing Deep Neural Networks (ICLR 2016) 2 PDF PDF

[9]: Basic16- Basic Deep NN and Robustness

read on: - 19 Jan 2017
Adversarial-Examples robustness visualizing Interpretable Certified-Defense
Presenter Papers Paper URL Our Slides
AE Intriguing properties of neural networks / PDF  
AE Explaining and Harnessing Adversarial Examples PDF  
AE Towards Deep Learning Models Resistant to Adversarial Attacks PDF  
AE DeepFool: a simple and accurate method to fool deep neural networks PDF  
AE Towards Evaluating the Robustness of Neural Networks by Carlini and Wagner PDF PDF
Data Basic Survey of ImageNet - LSVRC competition URL PDF
Understand Understanding Black-box Predictions via Influence Functions PDF  
Understand Deep inside convolutional networks: Visualising image classification models and saliency maps PDF  
Understand BeenKim, Interpretable Machine Learning, ICML17 Tutorial [^1] PDF  
provable Provable defenses against adversarial examples via the convex outer adversarial polytope, Eric Wong, J. Zico Kolter, URL  

[10]: Basic16- Basic Deep NN with Memory

read on: - 18 Jan 2017
memory NTM seq2seq pointer set attention meta-learning Few-Shot matching net metric-learning
Presenter Papers Paper URL Our Slides
seq2seq Sequence to Sequence Learning with Neural Networks PDF  
Set Pointer Networks PDF  
Set Order Matters: Sequence to Sequence for Sets PDF  
Point Attention Multiple Object Recognition with Visual Attention PDF  
Memory End-To-End Memory Networks PDF Jack Survey
Memory Neural Turing Machines PDF  
Memory Hybrid computing using a neural network with dynamic external memory PDF  
Muthu Matching Networks for One Shot Learning (NIPS16) 1 PDF PDF
Jack Meta-Learning with Memory-Augmented Neural Networks (ICML16) 2 PDF PDF
Metric ICML07 Best Paper - Information-Theoretic Metric Learning PDF  

[11]: Basic16- Basic DNN Embedding we read for Ranking/QA

read on: - 12 Jan 2017
embedding recommendation QA graph relational
Presenter Papers Paper URL Our Slides
QA Learning to rank with (a lot of) word features PDF  
Relation A semantic matching energy function for learning with multi-relational data PDF  
Relation Translating embeddings for modeling multi-relational data PDF  
QA Reading wikipedia to answer open-domain questions PDF  
QA Question answering with subgraph embeddings PDF  

[12]: Basic16- Basic DNN Reads we finished for NLP/Text

read on: - 12 Jan 2017
embedding text BERT seq2seq attention NLP curriculum BackProp relational
Presenter Papers Paper URL Our Slides
NLP A Neural Probabilistic Language Model PDF  
Text Bag of Tricks for Efficient Text Classification PDF  
Text Character-level Convolutional Networks for Text Classification PDF  
NLP BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding PDF  
seq2seq Neural Machine Translation by Jointly Learning to Align and Translate PDF  
NLP Natural Language Processing (almost) from Scratch PDF  
Train Curriculum learning PDF  
Muthu NeuroIPS Embedding Papers survey 2012 to 2015 NIPS PDF
Basics Efficient BackProp PDF  

Here is a name list of posts!


A general survey

less than 1 minute read

Presenter Papers Paper URL Our Slides Dr Qi Survey of Recent DeepLearning to 12 Groups / PDF ...

Basic16- DNN to be Scalable

4 minute read

Presenter Papers Paper URL Our Slides scalable Sanjiv Kumar (Columbia EECS 6898), Lecture: Intro...