Here is a list of accepted papers and scheduled presentations.
https://docs.google.com/spreadsheets/u/1/d/1RU2y-iuzwtAR_hn4V9yz1qpZSiElm3iaCpUoDJ-vfvQ/htmlview?sle=true#
https://docs.google.com/spreadsheets/u/1/d/1RU2y-iuzwtAR_hn4V9yz1qpZSiElm3iaCpUoDJ-vfvQ/htmlview?sle=true#
Foundations of Machine Learning - A Great Book on Machine Learning
By Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
MIT Press, Second Edition, 2018
Dr Mehryar Mohri is a Professor of Computer Science and Mathematics at Courant Institute of Mathematical Sciences, New York University
"This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms."
Online Edition:
https://mitpress.ublish.com/ereader/7093/?preview#page/Cover
By Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
MIT Press, Second Edition, 2018
Dr Mehryar Mohri is a Professor of Computer Science and Mathematics at Courant Institute of Mathematical Sciences, New York University
"This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms."
Online Edition:
https://mitpress.ublish.com/ereader/7093/?preview#page/Cover
Deep learning and the Schrödinger equation
By Mills et al.: https://arxiv.org/abs/1702.01361
#ArtificialInteligence #Science #DeepLearning #MachineLearning #Physics #MontrealAI
By Mills et al.: https://arxiv.org/abs/1702.01361
#ArtificialInteligence #Science #DeepLearning #MachineLearning #Physics #MontrealAI
Translatotron is the first end-to-end model that can directly translate speech from one language into speech in another language. It is also able to retain the source speaker’s voice in the translated speech.
https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html
https://ai.googleblog.com/2019/05/introducing-translatotron-end-to-end.html
research.google
Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model
Posted by Ye Jia and Ron Weiss, Software Engineers, Google AI Speech-to-speech translation systems have been developed over the past several decade...
Meta-Learning with Differentiable Convex Optimization #CVPR2019 Oral
Few-shot learning SoTA on miniImageNet, tieredImageNet, CIFAR-FS, and FC100
Github
https://github.com/kjunelee/MetaOptNet
ArXiv
https://arxiv.org/abs/1904.03758
Few-shot learning SoTA on miniImageNet, tieredImageNet, CIFAR-FS, and FC100
Github
https://github.com/kjunelee/MetaOptNet
ArXiv
https://arxiv.org/abs/1904.03758
GitHub
GitHub - kjunelee/MetaOptNet: Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral) - GitHub - kjunelee/MetaOptNet: Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
Garcez et al.: https://arxiv.org/abs/1905.06088
#ArtificialIntelligence #DeepLearning #MachineLearning
Garcez et al.: https://arxiv.org/abs/1905.06088
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Neural-Symbolic Computing: An Effective Methodology for Principled...
Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities, but also over...
On Variational Bounds of Mutual Information
Poole et al.: https://arxiv.org/abs/1905.06922
#ArtificialIntelligence #DeepLearning #MachineLearning
Poole et al.: https://arxiv.org/abs/1905.06922
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
On Variational Bounds of Mutual Information
Estimating and optimizing Mutual Information (MI) is core to many problems in machine learning; however, bounding MI in high dimensions is challenging. To establish tractable and scalable...
BERT Rediscovers the Classical NLP Pipeline
Tenney et al.: https://arxiv.org/abs/1905.05950
#artificialintelligence #bert #machinelearning #nlp
Tenney et al.: https://arxiv.org/abs/1905.05950
#artificialintelligence #bert #machinelearning #nlp
arXiv.org
BERT Rediscovers the Classical NLP Pipeline
Pre-trained text encoders have rapidly advanced the state of the art on many NLP tasks. We focus on one such model, BERT, and aim to quantify where linguistic information is captured within the...
Does the brain represent words? An evaluation of brain decoding studies of language under... (link: https://arxiv.org/abs/1806.00591) arxiv.org/abs/1806.00591
Number-State Preserving Tensor Networks as Classifiers for Supervised Learning
Glen Evenbly: https://arxiv.org/abs/1905.06352
#QuantumPhysics #DeepLearning #MachineLearning
Glen Evenbly: https://arxiv.org/abs/1905.06352
#QuantumPhysics #DeepLearning #MachineLearning
Deep Compressed Sensing
Wu et al.: https://arxiv.org/pdf/1905.06723.pdf
#deeplearning #generativeadversarialnetworks #technology
Wu et al.: https://arxiv.org/pdf/1905.06723.pdf
#deeplearning #generativeadversarialnetworks #technology
Video on self supervised learning of speech representations
By Mirco Ravanelli: https://youtu.be/1zjUmY8L5TU
#deeplearning #selfsupervisedlearning #unsupervisedlearning
By Mirco Ravanelli: https://youtu.be/1zjUmY8L5TU
#deeplearning #selfsupervisedlearning #unsupervisedlearning
YouTube
Toward Unsupervised Learning of Speech Representations
In this presentation, I first introduce unsupervised/self-supervised learning. Then, I describe some of my recent works that aim to learn general and robust self-supervised speech representations.
Using Deep Learning and TensorFlow Object Detection API for Corrosion Detection and Localization
https://blog.floydhub.com/localize-and-detect-corrosion-with-tensorflow-object-detection-api/
https://blog.floydhub.com/localize-and-detect-corrosion-with-tensorflow-object-detection-api/
FloydHub Blog
Using TensorFlow Object Detection API for Corrosion Detection and Localization
While computer vision techniques have been used with limited success for detecting corrosion from images, Deep Learning has opened up whole new possibilities
Discrete Flows: Invertible Generative Models of Discrete Data
Tran et al.: https://openreview.net/forum?id=rJlo4UIt_E
#ArtificialIntelligence #DeepLearning #GenerativeModels
Tran et al.: https://openreview.net/forum?id=rJlo4UIt_E
#ArtificialIntelligence #DeepLearning #GenerativeModels
openreview.net
Discrete Flows: Invertible Generative Models of Discrete Data
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show...
Giovani in azione: Mirco Ravanelli – Di Astrid Panizza
12/05/2019
Sulla strada dei cervelli in fuga: dal Trentino al Canada per ricerche sull’intelligenza artificiale
https://www.ladigetto.it/rubriche/giovani_in_azione/87204-giovani-in-azione%3A-mirco-ravanelli-%E2%80%93-di-astrid-panizza.html
12/05/2019
Sulla strada dei cervelli in fuga: dal Trentino al Canada per ricerche sull’intelligenza artificiale
https://www.ladigetto.it/rubriche/giovani_in_azione/87204-giovani-in-azione%3A-mirco-ravanelli-%E2%80%93-di-astrid-panizza.html
ICLR 2019 posters
By Jonathan Binas and Avital Oliver: https://postersession.ai
#deeplearning #ICLR2019 #technology
By Jonathan Binas and Avital Oliver: https://postersession.ai
#deeplearning #ICLR2019 #technology
"Cellular automata as convolutional neural networks"
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
arXiv.org
Cellular automata as convolutional neural networks
Deep learning techniques have recently demonstrated broad success in predicting complex dynamical systems ranging from turbulence to human speech, motivating broader questions about how neural...
Machine Learning in High Energy Physics Community White Paper
Albertsson et al.: https://arxiv.org/abs/1807.02876
#machinelearning #physics #technology
Albertsson et al.: https://arxiv.org/abs/1807.02876
#machinelearning #physics #technology
arXiv.org
Machine Learning in High Energy Physics Community White Paper
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of...
Playing Atari with Six Neurons
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
arXiv.org
Playing Atari with Six Neurons
Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting...
Machine Learning in High Energy Physics Community White Paper
Albertsson et al.: https://arxiv.org/abs/1807.02876
#machinelearning #physics #technology
Albertsson et al.: https://arxiv.org/abs/1807.02876
#machinelearning #physics #technology
arXiv.org
Machine Learning in High Energy Physics Community White Paper
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of...
A New Confidence Interval for the Mean of a Bounded Random Variable
Erik Learned-Miller and Philip S. Thomas: https://arxiv.org/abs/1905.06208
#Statistics #MachineLearning #Probability
Erik Learned-Miller and Philip S. Thomas: https://arxiv.org/abs/1905.06208
#Statistics #MachineLearning #Probability