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
https://www.profillic.com/paper/arxiv:1902.08646
The paper introduces the Pytorch-based open source framework OpenKiwi for translation quality estimation
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
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
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
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
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
Propose bivariate normal distribution for object detection representation.
Benefits detection of highly-overlapping objects and downstream tracking
https://arxiv.org/abs/1907.12929
Forwarded from Lex Fridman
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/
link: https://hcai.mit.edu/human-side-of-tesla-autopilot/
Forwarded from Lex Fridman
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
Top 10 of 2019: AI and Deep Learning Content Recommended by Experts
https://medium.com/@teamrework/top-10-of-2019-ai-and-deep-learning-content-recommended-by-experts-58a19166e5bd
https://medium.com/@teamrework/top-10-of-2019-ai-and-deep-learning-content-recommended-by-experts-58a19166e5bd
Medium
Top 10 of 2019: AI and Deep Learning Content Recommended by Experts
How often do you have the time to pick up a book, or listen to a podcast whilst giving it your complete undivided attention? The summer is…
Multiscale Representations for Manifold-Valued Data
Rahman et al.: https://statweb.stanford.edu/~symmlab/SymmPaper.pdf
#SymmetricSpace #Wavelets #Denoising
Rahman et al.: https://statweb.stanford.edu/~symmlab/SymmPaper.pdf
#SymmetricSpace #Wavelets #Denoising
Visionary GPU Architecture Paper Wins SC19 Test of Time Award
Blog by Jeffrey K. Hollingsworth : https://sc19.supercomputing.org/2019/08/13/visionary-gpu-architecture-paper-wins-sc19-test-of-time-award/
#ArtificialIntelligence #DeepLearning #GPU
Blog by Jeffrey K. Hollingsworth : https://sc19.supercomputing.org/2019/08/13/visionary-gpu-architecture-paper-wins-sc19-test-of-time-award/
#ArtificialIntelligence #DeepLearning #GPU
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective
Tom Everitt and Marcus Hutter : https://arxiv.org/abs/1908.04734
#ArtificialIntelligence #MachineLearning #ReinforcementLearning
Tom Everitt and Marcus Hutter : https://arxiv.org/abs/1908.04734
#ArtificialIntelligence #MachineLearning #ReinforcementLearning
Creating a Pop Music Generator with the Transformer
Blog by Andrew Shaw : https://towardsdatascience.com/creating-a-pop-music-generator-with-the-transformer-5867511b382a
#Music #DeepLearning #ArtificialIntelligence
Blog by Andrew Shaw : https://towardsdatascience.com/creating-a-pop-music-generator-with-the-transformer-5867511b382a
#Music #DeepLearning #ArtificialIntelligence
Medium
Creating a Pop Music Generator with the Transformer
Train a Deep Learning model to generate pop music. Play with the results here — https://musicautobot.com.
SLIDES
Transfer Learning in Natural Language Processing
June 2, 2019
NAACL-HLT 2019
https://docs.google.com/presentation/d/1fIhGikFPnb7G5kr58OvYC3GN4io7MznnM0aAgadvJfc/edit#slide=id.g5888218f39_177_4
Transfer Learning in Natural Language Processing
June 2, 2019
NAACL-HLT 2019
https://docs.google.com/presentation/d/1fIhGikFPnb7G5kr58OvYC3GN4io7MznnM0aAgadvJfc/edit#slide=id.g5888218f39_177_4
Google Docs
Transfer Learning in Natural Language Processing
Transfer Learning in Natural Language Processing June 2, 2019 NAACL-HLT 2019 1 Sebastian Ruder Matthew Peters Swabha Swayamdipta Thomas Wolf
The Math behind Neural Networks: Part 1 - The Rosenblatt Perceptron
https://www.codeproject.com/Articles/4047091/The-Math-behind-Neural-Networks-Part-1-The-Rosenbl
https://www.codeproject.com/Articles/4047091/The-Math-behind-Neural-Networks-Part-1-The-Rosenbl
CodeProject
The Math behind Neural Networks: Part 1 - The Rosenblatt Perceptron
A try it yourself guide to the basic math behind perceptrons
Why BLEU score sucks for evaluating translation systems.
(Or rather, why BLEU score works fine when you translation system sucks, but sucks when it's good).
https://arxiv.org/abs/1908.05204
(Or rather, why BLEU score works fine when you translation system sucks, but sucks when it's good).
https://arxiv.org/abs/1908.05204
Welcome SUPERGLUE from Facebook AI, DeepMind, University of Washington and New York University.
It comprises new ways to test creative approaches on a range of difficult NLP tasks and serves a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.
Made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges .
Read https://arxiv.org/pdf/1905.00537.pdf
It comprises new ways to test creative approaches on a range of difficult NLP tasks and serves a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.
Made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges .
Read https://arxiv.org/pdf/1905.00537.pdf
Mesh R-CNN
Gkioxari et al.: https://arxiv.org/abs/1906.02739
#ArtificialIntelligence #DeepLearning #MachineLearning
Gkioxari et al.: https://arxiv.org/abs/1906.02739
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Mesh R-CNN
Rapid advances in 2D perception have led to systems that accurately detect objects in real-world images. However, these systems make predictions in 2D, ignoring the 3D structure of the world....
If you are working on new ideas for video understanding and recognition, you can consider submitting your new works or recently published works to the first Holistic Video Understanding Workshop at ICCV 2019!
https://holistic-video-understanding.github.io/workshops/iccv2019.html?fbclid=IwAR2iDSViwLOLuyHa99Ho2FjZ6oQs4toskiBq0gX4W1wEsApuShPq-aFlXBo
https://holistic-video-understanding.github.io/workshops/iccv2019.html?fbclid=IwAR2iDSViwLOLuyHa99Ho2FjZ6oQs4toskiBq0gX4W1wEsApuShPq-aFlXBo
From Recognition to Cognition: Visual Commonsense Reasoning
Zellers et al.: https://arxiv.org/abs/1811.10830
#ArtificialIntelligence #DeepLearning #MachineLearning
Zellers et al.: https://arxiv.org/abs/1811.10830
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
From Recognition to Cognition: Visual Commonsense Reasoning
Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals,...
Welcome SUPERGLUE from Facebook AI, DeepMind, University of Washington and New York University.
It comprises new ways to test creative approaches on a range of difficult NLP tasks and serves a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.
Made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges .
Read https://arxiv.org/pdf/1905.00537.pdf
It comprises new ways to test creative approaches on a range of difficult NLP tasks and serves a series of benchmark tasks to measure the performance of modern, high performance language-understanding AI.
Made on the premise that deep learning models for conversational AI have “hit a ceiling” and need greater challenges .
Read https://arxiv.org/pdf/1905.00537.pdf