The Mathematics of Machine Learning by UC Berkeley: Written by - Garret Thomas
Link: https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI

HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

Human evaluation for generative models have been ad-hoc.

They propose a standard human benchmark for generative realism that is grounded in psychophysics research in perception.

https://arxiv.org/abs/1904.01121
https://hype.stanford.edu/

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Yu et al.: https://arxiv.org/abs/1909.05378
#ArtificialIntelligence #Database #MachineLearning

Big moment! Google claims to have reached quantum supremacy, taking 3 mins to do a calculation classical computers would take 10,000 YEARS to do.

https://www.ft.com/content/b9bb4e54-dbc1-11e9-8f9b-77216ebe1f17
https://t.iss.one/ArtificialIntelligenceArticles

Just read the #quantum #supremacy (#quantumsupremacy) manuscript which features a bold outlook into the #future. Certainly a very nice piece of work performed on a #superconducting #device featuring 53 #qubits with high fidelities.

Artificial intelligence probes dark matter in the universe
By Oliver Morsch : https://ethz.ch/en/news-and-events/eth-news/news/2019/09/artificial-intelligence-probes-dark-matter-in-the-universe.html
#ArtificialIntelligence #DeepLearning #Physics

Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Li et al.: https://arxiv.org/abs/1909.09300
#ArtificialIntelligence #DeepLearning #MachineLearning

Stanford Machine Learning Notes
Andrew Ng
https://www.holehouse.org/mlclass/

Andrew NG the pioneer of machine learning and deep learning online courses <3 <3 <3

https://www.youtube.com/watch?v=TbiGfPBzbko

Since "2001: A Space Odyssey," people have wondered: could machines like HAL 9000 eventually exist that can process information with human-like intelligence?
Researchers at Michigan State University say that true, human-level intelligence remains a long way off, but their new paper published in The American Naturalist explores how computers could begin to evolve learning in the same way as natural organisms did—with implications for many fields, including artificial intelligence.

https://phys.org/news/2019-09-evolution-key-artificial-intelligence.html

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning

Using Information Gain for the Unsupervised Training of Excitatory Neurons
https://towardsdatascience.com/using-information-gain-for-the-unsupervised-training-of-excitatory-neurons-e069eb90245b

This is an attempt to modify Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook's code into PyTorch
GitHub, by SDS Data Science Group, IIT Roorkee: https://github.com/dsgiitr/d2l-pytorch
#datascience #deeplearning #pytorch

Mesh R-CNN
Gkioxari et al.: https://arxiv.org/abs/1906.02739
#ArtificialIntelligence #DeepLearning #MachineLearning

7th HLF – Turing Lecture: Yoshua Bengio
"This lecture will look back at some of the principles behind the recent successes of deep learning as well as acknowledge current limitations, and finally propose research directions to build on top of this progress and towards human-level AI. "
Video: https://youtu.be/llGG62fNN64
#ArtificialIntelligence #DeepLearning
@ArtificialIntelligenceArticles