Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/rahulbhalley/favorite-research-papers

Listing my favorite research papers 📝 from different fields as I read them.
https://github.com/rahulbhalley/favorite-research-papers

artificial-intelligence deep-learning generative-adversarial-network generative-model image-classification machine-learning neural-network research-paper speech-recognition style-transfer transfer-learning

Last synced: about 2 months ago
JSON representation

Listing my favorite research papers 📝 from different fields as I read them.

Awesome Lists containing this project

README 

        
# Favorite Research Papers



This is a curated list of my favorite research papers 📝 from different computer science subfields.



Fields I explore in the search for *true* intelligence:



- [Neural Networks](https://github.com/rahulbhalley/favorite-research-papers#neural-networks)

- [Neural Reinforcement Learning](https://github.com/rahulbhalley/favorite-research-papers#neural-reinforcement-learning)

- [Computational Neuroscience](https://github.com/rahulbhalley/favorite-research-papers#computational-neuroscience)



Computation-oriented fields I like to keep an eye on:



- [Quantum Computation](https://github.com/rahulbhalley/favorite-research-papers#quantum-computation)

- [Blockchain](https://github.com/rahulbhalley/favorite-research-papers#blockchain)






## Neural Networks



This sub-field of AI is concerned with wide and deep neural networks based computational approach to intelligence in machines with the use of large amounts of data. And below is list of my favorite neural networks papers.



### Audio



- WaveNet: A Generative Model for Raw Audio [[arXiv](https://arxiv.org/abs/1609.03499)] (2016)



### Frameworks



- Automatic differentiation in PyTorch [[OpenReview](https://openreview.net/pdf?id=BJJsrmfCZ)][[official website](https://pytorch.org)] (2017)

- TensorFlow: A system for large-scale machine learning [[arXIv](https://arxiv.org/abs/1605.08695)][[official website](https://www.tensorflow.org/)] (2016)



### Generative Modeling



#### Generative Adversarial Networks



- Generative Adversarial Networks [[arXiv](https://arxiv.org/abs/1406.2661)] (2014)

- Conditional Generative Adversarial Nets [[arXiv](https://arxiv.org/abs/1411.1784)] (2014)

- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [[arXiv](https://arxiv.org/abs/1511.06434)] (2015)

- Progressive Growing of GANs for Improved Quality, Stability, and Variation [[arXiv](https://arxiv.org/abs/1710.10196)][[code](https://github.com/rahulbhalley/Progressive-Growing-of-GANs)] (2017)

- FaceGANs: Stable Generative Adversarial Networks with High-Quality Images [[ICLR 2018 Workshop](https://openreview.net/forum?id=HJn_vKyPM)] (2018)

- C-RNN-GAN: Continuous recurrent neural networks with adversarial training [[arXiv](https://arxiv.org/abs/1611.09904)] (2016)

- Generative Adversarial Text to Image Synthesis [[arXiv](https://arxiv.org/abs/1605.05396)] (2016)

- NIPS 2016 Tutorial: Generative Adversarial Networks [[arXiv](https://arxiv.org/abs/1701.00160)] (2016)

- Adversarial Audio Synthesis [[arXiv](https://arxiv.org/abs/1802.04208)] (2018)

- Improved Techniques for Training GANs [[arXiv](https://arxiv.org/abs/1606.03498)] (2016)

- Wasserstein GAN [[arXiv](https://arxiv.org/abs/1701.07875)] (2017)

- Improved Training of Wasserstein GANs [[arXiv](https://arxiv.org/abs/1704.00028)] (2017)

- Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect [[arXiv](https://arxiv.org/abs/1803.01541)] (2018)

- Training Generative Adversarial Networks Via Turing Test [[arXiv](https://arxiv.org/abs/1810.10948)] (2018)

- GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz Constraint [[arXiv](https://arxiv.org/abs/1811.07296)] (2018)

- A Large-Scale Study on Regularization and Normalization in GANs [[arXiV](https://arxiv.org/abs/1807.04720)] [2018]



#### Image-to-Image Translation



- Artist Style Transfer Via Quadratic Potential [[arXiv](https://arxiv.org/abs/1902.11108)] [[code](https://github.com/rahulbhalley/cyclegan-plus-plus)] (2019)



### Image Classification



- ImageNet Classification with Deep Convolutional Neural Networks [[NIPS](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)][[code](https://github.com/rahulbhalley/AlexNet-TensorFlow)] (2012)



### Natural Language Processing



- Quasi-Recurrent Neural Networks [[arXiv](https://arxiv.org/abs/1611.01576)] (2016)



### Meta Learning / Transfer Learning / Learning to Learn / Domain Adaptation



- DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition [[arXiv](https://arxiv.org/abs/1310.1531)] (2013)



### Reviews



- Machine learning: Trends, perspectives, and prospects [[Science](http://science.sciencemag.org/content/349/6245/255)] (2015)

- Deep learning [[Nature](https://www.nature.com/articles/nature14539)] (2015)



### Speech Recognition



- ASR — A real-time speech recognition on portable devices [[IEEE](https://ieeexplore.ieee.org/document/7749004/)] (2016)



### Style Transfer



- A Neural Algorithm of Artistic Style [[arXiv](https://arxiv.org/abs/1508.06576)] (2015)



### Training Techniques



- Learning representations by back-propagating errors [[Nature](https://www.nature.com/articles/323533a0)] (1986)

- Dropout: A Simple Way to Prevent Neural Networks from Overfitting [[JMLR](http://jmlr.org/papers/v15/srivastava14a.html)] (2014)



### Data Visualization



- Visualizing Data using t-SNE [[JMLR](http://www.jmlr.org/papers/v9/vandermaaten08a.html)] (2008)






## Neural Reinforcement Learning



### Reviews



- Reinforcement Learning, Fast and Slow [[Trends in Cognitive Sciences](https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30061-0)][[My Thoughts 💭](https://github.com/rahulbhalley/favorite-research-papers/blob/master/reinforcement-learning-fast-and-slow.md)] (2019)






## Computational Neuroscience






## Quantum Computation






## Blockchain