Machine Learning Internships in India

https://verzeo.in/machine-learning-internship-in-india

AutoDL 2019


https://autodl.chalearn.org/

What’s New in Deep Learning Research: Learning and Teaching the West World Way
#towardsdatascience #datascience #news #learning
https://towardsdatascience.com/whats-new-in-deep-learning-research-learning-and-teaching-the-west-world-way-659892b2e452

CVPR 2018 open access

https://openaccess.thecvf.com/content_cvpr_2018/html/Ulyanov_Deep_Image_Prior_CVPR_2018_paper.html

Deep Kernel Learning for Clustering
Wu et al.: https://arxiv.org/pdf/1908.03515v1.pdf
#DeepLearning #MachineLearning #NeuralNetworks

One-shot Face Reenactment"
Zhang et al.: https://arxiv.org/abs/1908.03251
Project: https://wywu.github.io/projects/ReenactGAN/OneShotReenact.html
GitHub: https://github.com/bj80heyue/One_Shot_Face_Reenactment
#ArtificialIntelligence #DeepLearning #MachineLearning

Looking to fall in love... with science? 😍 Help scientists train machines to study stroke lesions by swiping on our app:

https://braindrles.us/#/

#citizenscience #braindr #braindrles #neuroscience #machinelearning #swipesforscience #openscience #OHBM2019

An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
Such et al.: https://arxiv.org/abs/1812.07069
Code: https://github.com/uber-research/atari-model-zoo
Blog: https://eng.uber.com/atari-zoo-deep-reinforcement-learning/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

MegatronLM: Training Billion+ Parameter Language Models Using GPU Model Parallelism
"... training an 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism on 512 GPUs, making it the largest transformer based language model ever trained at 24x the size of BERT and 5.6x the size of GPT-2."
Blog by NVIDIA Applied Deep Learning Research : https://nv-adlr.github.io/MegatronLM
Code: https://github.com/nvidia/megatron-lm
#ArtificialIntelligence #DeepLearning #NLP #PyTorch #Transformer

"There has been surprisingly little mainstream discussion about how the techniques we classify as AI actually work.”

Andrew Ng and Derrick Harris discuss enterprise AI in just 15 minutes: https://content.pivotal.io/intersect/ai-in-15-minutes

The functional organisation of the hippocampus along its long axis is gradual and predicts recollection
https://reader.elsevier.com/reader/sd/pii/S0010945219301832?token=7ED07A1848AFEC6DC2EA8E0B689CC9EB30478F019977CF3DB31E898DE8DFF75456605EE621D0E2B7D2212D3FD9E00C72

Understanding XLNet
https://www.borealisai.com/en/blog/understanding-xlnet/

ICYMI: Best demo paper from ACL 2019 (super recent)

https://www.profillic.com/paper/arxiv:1902.08646

The paper introduces the Pytorch-based open source framework OpenKiwi for translation quality estimation

Generating Diverse High-Fidelity Images with VQ-VAE-2

Ali Razavi, Aaron van den Oord, Oriol Vinyals : https://arxiv.org/abs/1906.00446

#DeepLearning #VariationalAutoEncoder #VAE

Project Euphonia’s Personalized Speech Recognition for Non-Standard Speech
Blog by Joel Shor and Dotan Emanuel : https://ai.googleblog.com/2019/08/project-euphonias-personalized-speech.html
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Is Deep Reinforcement Learning Really Superhuman on Atari?
Marin Toromanoff, Emilie Wirbel, Fabien Moutarde : https://arxiv.org/abs/1908.04683
#deeplearning #machinelearning #reinforcementlearning

Object as Distribution #NeurIPS2019


Propose bivariate normal distribution for object detection representation.
Benefits detection of highly-overlapping objects and downstream tracking

https://arxiv.org/abs/1907.12929

The following is our paper on driver functional vigilance during use of Tesla Autopilot driver assistance system. We analyzed 18,928 Autopilot disengagements. 3+ years of hard work with an incredible research team at MIT. Example videos out next week.

link: https://hcai.mit.edu/human-side-of-tesla-autopilot/

If a neural network generates an image, who owns the copyright? The owner of the dataset that the net was trained on? The designer of the network architecture? The person running the code? Or... the AI system itself? @lexfridman