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

Training Image Classification/Recognition models based on Deep Learning & Transfer Learning with ML.NET - Cesar De la Torre

https://devblogs.microsoft.com/cesardelatorre/training-image-classification-recognition-models-based-on-deep-learning-transfer-learning-with-ml-net/

Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented... https://arxiv.org/abs/1909.09148

https://jacobbuckman.com/2019-09-23-automation-via-reinforcement-learning/?

Great applications for the healthcare industry: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle

https://www.profillic.com/paper/arxiv:1909.08986

"Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle"

[Research] A New Method for Quantizing BERT Models
Summary: https://medium.com/ai%C2%B3-theory-practice-business/a-new-method-for-quantizing-bert-models-bc770a95f26f
Read the full paper: https://arxiv.org/abs/1909.05840v1

Data-driven algorithm design, reducing machine learning bias with truncated statistics, and the regularization effect of initial large learning rates—take a deep dive into these topics with Machine Learning Dept. at Carnegie Mellon University’s Nina Balcan, #MIT’s Costis Daskalakis, and #Stanford’s Tengyu Ma: via Microsoft Research
https://www.microsoft.com/en-us/research/video/ai-institute-geometry-of-deep-learning-2019-day-2-session-2/?OCID=msr_video_mlbias_aiinst_fb

Distributed Machine Learning on Mobile Devices: A Survey [28pp]


https://arxiv.org/abs/1909.08329v1

Kalman Filtering with Gaussian Processes Measurement Noise. https://arxiv.org/abs/1909.10582