Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation. https://arxiv.org/abs/2001.03637

Backward Feature Correction: How Deep Learning Performs Deep Learning
https://arxiv.org/abs/2001.04413

"My Top 10 Deep RL Papers of 2019"
Blog by Robert Tjarko Lange: https://roberttlange.github.io/posts/2019/12/blog-post-9/
#ArtificialIntelligence #Deeplearning #ReinforcementLearning

Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings
Qi Li, Long Mai, Anh Nguyen: https://anhnguyen.me/project/biggan-am/
#DeepLearning #GenerativeAdversarialNetworks #GAN

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Github: https://github.com/EndlessSora/DeeperForensics-1.0
Paper: https://arxiv.org/abs/2001.03024v1
Project Page: https://liming-jiang.com/projects/DrF1/DrF1.html
Video: https://youtu.be/b6iKqkJht38

Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
Yang et al.: https://arxiv.org/abs/2001.02890
#ArtificialIntelligence #DeepLearning #MachineLearning

Hello, Millionaire Mohammad Rastegari! :) Super congrats to the team!

https://techcrunch.com/2020/01/15/apple-buys-edge-based-ai-startup-xnor-ai-for-a-reported-200m/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAIwhp3HVzxPc6-Y-uSL214IVtdjF3myhyYl25Ua_pHK4AmqY31NL1qDSuISenFDRbvNWSlnnG0W7Ui61QhV2WM_cOS4p1bEQLU4m7V2cBrGm1dAnpNd0bv4a79aKHQuKjyizkHSdZSfOftAFFCG_OM-C1cNhxfBHqGwfizV2yTya

DDSP: Differentiable Digital Signal Processing
Engel et al.
⌨️ Blog: https://magenta.tensorflow.org/ddsp
🎵 Examples: https://g.co/magenta/ddsp-examples
⏯ Colab: https://g.co/magenta/ddsp-demo
💻 Code: https://github.com/magenta/ddsp
📝 Paper: https://g.co/magenta/ddsp-paper
#ArtificialIntelligence #TensorFlow #SignalProcessing

"Everybody’s Talkin’: Let Me Talk as You Want"
Paper pdf: https://arxiv.org/pdf/2001.05201.pdf
Github: https://wywu.github.io/projects/EBT/EBT.html
Youtube: https://youtu.be/tNPuAnvijQk
This paper presents a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video.

Bayesian Deep Learning Benchmarks
Oxford Applied and Theoretical Machine Learning Group : https://github.com/OATML/bdl-benchmarks
#Bayesian #Benchmark #DeepLearning

Reasoning about Time and Knowledge in Neural Symbolic Learning Systems
Artur Garcez and Luis C. Lamb : https://papers.nips.cc/paper/2490-reasoning-about-time-and-knowledge-in-neural-symbolic-learning-systems
#ArtificialIntelligence #Reasoning #SymbolicAI

Reformer: The Efficient Transformer
https://ai.googleblog.com/2020/01/reformer-efficient-transformer.html

On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks
Michela Paganini, Jessica Forde: https://arxiv.org/abs/2001.05050
#ArtificialIntelligence #MachineLearning #NeuralNetwork

Open Questions about Generative Adversarial Networks
What we’d like to find out about GANs that we don’t know yet. https://distill.pub/2019/gan-open-problems/

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2019
https://insidebigdata.com/2020/01/16/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-december-2019/

Free Online Course: Fundamentals of Machine Learning from Complexity Explorer Class Central
https://www.complexityexplorer.org/courses/81-fundamentals-of-machine-learning

Neuroscience and Artificial Intelligence Are More Linked Than You'd Expect
https://interestingengineering.com/neuroscience-and-artificial-intelligence-are-more-linked-than-youd-expect

CvxNets: Learnable Convex Decomposition by Geoffrey Hinton,

Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Andrea Tagliasacchi : https://arxiv.org/abs/1909.05736

Neuroimaging may become a key tool in the diagnosis of mental health disorders:
https://neurosciencenews.com/mental-health-neuroimaging-15502/

108-page survey paper/book on Deep Learning Techniques for Music Generation. https://arxiv.org/pdf/1709.01620v1.pdf

"Optuna: A Next-generation Hyperparameter Optimization Framework"
Akiba et al.: https://arxiv.org/abs/1907.10902
#ArtificialIntelligence #DataScience #MachineLearning