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

Backward Feature Correction: How Deep Learning Performs Deep Learning
Zeyuan Allen-Zhu, Yuanzhi Li: https://arxiv.org/abs/2001.04413
#ArtificialIntelligence #DeepLearning #MachineLearning

List of Global Artificial Intelligence / Machine Learning Conferences in 2020
https://www.marktechpost.com/2020/01/17/list-of-global-artificial-intelligence-machine-learning-conferences-in-2020/

Deep learning continues to improve at astounding rates. In 2017 it costed $1,000 to train ResNet50 on the cloud, now it costs $10.

Thanks to AI accelerators and 'unlimited' compute in the cloud, AI algorithms are eating up 10x the amount of FLOPs per year.

Evolution of circuits for machine learning
https://www.nature.com/articles/d41586-020-00002-x

Geoffrey Hinton: What is wrong with convolutional neural nets?
https://youtube.com/watch?v=Jv1VDdI4vy4
#geoffreyhinton

Selective Brain Damage: Measuring the Disparate Impact of Model Compression
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome: https://weightpruningdamage.github.io
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Connecting Vision and Language with Localized Narratives
Pont-Tuset et al.: https://arxiv.org/abs/1912.03098
#ArtificialIntelligence #DeepLearning #MachineLearning