Deep Learning Tools
PDF Version: https://drive.google.com/file/d/1XhngKISDpQgwGlvU-hjXWZb_qfyIYjqN/view

A curated list of decision, classification and regression tree research papers from the last 30 years with implementations. It covers NeurIPS, ICML, ICLR, KDD, ICDM, CIKM, AAAI etc.
https://github.com/benedekrozemberczki/awesome-decision-tree-papers

A key conference quality indicator is low paper acceptance rates. The CVPR 2019 paper acceptance rate dropped to 25.1 percent from last year’s 29.6 percent 🤓☝️

The list of all 1300 research papers accepted for CVPR 2019 is available here: https://openaccess.thecvf.com/CVPR2019.py

Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃

#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning

ICYMI: Prof. Strang wants to tell you about his newest course 18.065 "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning." It's all free on OCW--->

https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/

Text2Scene: Generating Compositional Scenes from Textual Descriptions
Tan et al.: https://arxiv.org/abs/1809.01110
#ArtificialIntelligence #DeepLearning #MachineLearning

Tackling Climate Change with Machine Learning
Rolnick et al.: https://arxiv.org/abs/1906.05433
#artificialintelligence #climatechange #climatecrisis #machinelearning

Colab notebooks tutorials for Swift for TensorFlow
GitHub by Zaid Alyafeai: https://github.com/zaidalyafeai/Swift4TF
#artificialintelligence #machinelearning #swift #tensorflow

Efficient Exploration via State Marginal Matching
Lee et al.: https://arxiv.org/abs/1906.05274
#ArtificialIntelligence #Robotics #MachineLearning

Best research paper award at our Debugging ML workshop -- "Similarity of Neural Network Representations Revisited" by Geoffrey Hinton , Mohammad Norouzi, Honglak Lee, and Simon Kornblith
https://arxiv.org/abs/1905.00414
#ICLR2019 https://t.iss.one/ArtificialIntelligenceArticles

Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction
Dornadula et al.: https://arxiv.org/pdf/1906.04876.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning

new paper from Andrew Ng , Yoshua Bengio ,Demis Hassabis , .... arxiv.org/abs/1906.05433 https://t.iss.one/ArtificialIntelligenceArticles

Tackling Climate Change with Machine Learning

Collaboration between #CarnegieMellon Carnegie Mellon University School of Computer Science, University of Pennsylvania, ETH Zürich, University of Colorado Boulder, Element AI, Mila, Université de Montréal, Harvard University, Mercator Research Institute, Technische Universit¨at Berlin, Massachusetts Institute of Technology (MIT), Cornell University, Stanford University, DeepMind, GoogleAI, Microsoft Research arxiv.org/abs/1906.05433 https://t.iss.one/ArtificialIntelligenceArticles

https://bit.ly/2KQTfzF

Face Recognition is the upcoming and modern challenge required in the machine learning in today's world. We have listed out some of the best Face Recogntion APIs which can be seamlessly integrated into your project to go one step further in image processing

AI Habitat: an advanced simulation platform for embodied AI research
Written by Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Dhruv Batra: https://ai.facebook.com/blog/open-sourcing-ai-habitat-an-simulation-platform-for-embodied-ai-research/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Cool paper from Microsoft Research team achieving SOTA provable L2-robustness on ImageNet by adversarially training a neural network convolved with Gaussian noise!

paper: https://arxiv.org/abs/1906.04584
code: https://github.com/Hadisalman/smoothing-adversarial
blog: https://decentdescent.org/smoothadv.html

I am excited to share work from my team at Facebook Reality Labs: the Replica Dataset - a high quality dataset of 18 3D reconstruction that has clean dense geometry, high resolution and high dynamic range textures, glass and mirror surface information, and semantic class and instance segmentation. See https://arxiv.org/abs/1906.05797 for more details. You can download Replica v1 now via https://github.com/facebookresearch/Replica-Dataset

This was a joint effort with FAIR and their awesome AI Habitat Simulator (https://aihabitat.org/). Here are two blog posts describing how Replica and AI Habtiat fit together to train the next generation of AI agents and assistants:
https://tech.fb.com/facebook-reality-labs-replica-simulations-help-advance-ai-and-ar/
https://ai.facebook.com/blog/open-sourcing-ai-habitat-an-simulation-platform-for-embodied-ai-research

Yann lecun :

FAIR and Facebook Reality Lab (FRL) have collaborated to release two interactive environments for trainin AI agents:
1. AI Habitat: fast simulation of indoor environments
2. Replica: visually realistic indoor environment
Blog posts:
https://ai.facebook.com/blog/open-sourcing-ai-habitat-an-simulation-platform-for-embodied-ai-research/
https://tech.fb.com/facebook-reality-labs-replica-simulations-help-advance-ai-and-ar/

SLIDES
Generating high Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling

Jacob Menick Nal Kalchbrenner
DeepMind Google Brain Amsterdam

https://drive.google.com/file/d/1bbJrQmCAjzkEZpumWQClo_qR3wBQFWD8/view?fbclid=IwAR2Z2UZAfqiw6o-2ctpCAOj8njzHnHc-sSfU3gMULKtzNQ2X0qXLhR5tYs0