Assessing the Quality of Long-Form Synthesized Speech
http://ai.googleblog.com/2019/09/assessing-quality-of-long-form.html
http://ai.googleblog.com/2019/09/assessing-quality-of-long-form.html
research.google
Assessing the Quality of Long-Form Synthesized Speech
Posted by Tom Kenter, Google Research, London Automatically generated speech is everywhere, from directions being read out aloud while you are dr...
Forwarded from Artificial Intelligence
📝 The paper: Adversarial Examples Are Not Bugs, They Are Features
video: https://www.youtube.com/watch?v=AOZw1tgD8dA
available here: http://gradientscience.org/adv/
article: https://distill.pub/2019/advex-bugs-discussion/
video: https://www.youtube.com/watch?v=AOZw1tgD8dA
available here: http://gradientscience.org/adv/
article: https://distill.pub/2019/advex-bugs-discussion/
YouTube
Adversarial Attacks on Neural Networks - Bug or Feature?
❤️ Support us on Patreon: https://www.patreon.com/TwoMinutePapers
📝 The paper "Adversarial Examples Are Not Bugs, They Are Features" is available here:
http://gradientscience.org/adv/
The Distill discussion article is available here:
https://distill.pub/2019/advex…
📝 The paper "Adversarial Examples Are Not Bugs, They Are Features" is available here:
http://gradientscience.org/adv/
The Distill discussion article is available here:
https://distill.pub/2019/advex…
Recursive Sketches for Modular Deep Learning
http://ai.googleblog.com/2019/09/recursive-sketches-for-modular-deep.html
http://ai.googleblog.com/2019/09/recursive-sketches-for-modular-deep.html
Googleblog
Recursive Sketches for Modular Deep Learning
Learning Cross-Modal Temporal Representations from Unlabeled Videos
http://ai.googleblog.com/2019/09/learning-cross-modal-temporal.html
http://ai.googleblog.com/2019/09/learning-cross-modal-temporal.html
Googleblog
Learning Cross-Modal Temporal Representations from Unlabeled Videos
17th September In Moscow MegaFon office will host another meetup. Speakers from Mail.Ru, Altinity, Couchbase and MegaFon will talk about Statefull in Kubernetes. Free admission.
For details and registration : https://pao-megafon--org.timepad.ru/event/1056036/
For details and registration : https://pao-megafon--org.timepad.ru/event/1056036/
Machine Learning for Physics and the Physics of Learning Tutorials
overview: https://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/
videos: https://www.ipam.ucla.edu/videos/
overview: https://www.ipam.ucla.edu/programs/workshops/machine-learning-for-physics-and-the-physics-of-learning-tutorials/
videos: https://www.ipam.ucla.edu/videos/
IPAM
Machine Learning for Physics and the Physics of Learning Tutorials - IPAM
5 Reasons to Learn Probability for Machine Learning
https://machinelearningmastery.com/why-learn-probability-for-machine-learning/
https://machinelearningmastery.com/why-learn-probability-for-machine-learning/
👍1
PyTorch Meta-learning Framework for Researchers
https://github.com/learnables/learn2learn
learn2learn is a PyTorch library for meta-learning implementations
http://learn2learn.net
https://github.com/learnables/learn2learn
learn2learn is a PyTorch library for meta-learning implementations
http://learn2learn.net
GitHub
GitHub - learnables/learn2learn: A PyTorch Library for Meta-learning Research
A PyTorch Library for Meta-learning Research. Contribute to learnables/learn2learn development by creating an account on GitHub.
Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
http://ai.googleblog.com/2019/09/using-deep-learning-to-inform.html
http://ai.googleblog.com/2019/09/using-deep-learning-to-inform.html
Google Research
Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
Posted by Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health (Updated May 18, 2020: The work described ...
A Gentle Introduction to Uncertainty in Machine Learning
https://machinelearningmastery.com/uncertainty-in-machine-learning/
https://machinelearningmastery.com/uncertainty-in-machine-learning/
MachineLearningMastery.com
A Gentle Introduction to Uncertainty in Machine Learning - MachineLearningMastery.com
Applied machine learning requires managing uncertainty.
There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any…
There are many sources of uncertainty in a machine learning project, including variance in the specific data values, the sample of data collected from the domain, and in the imperfect nature of any…
Facebook Research at Interspeech 2019
https://ai.facebook.com/blog/facebook-research-at-interspeech-2019/
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
https://research.fb.com/publications/sequence-to-sequence-speech-recognition-with-time-depth-separable-convolutions/
Unsupervised Singing Voice Conversion
https://research.fb.com/publications/unsupervised-singing-voice-conversion/
https://ai.facebook.com/blog/facebook-research-at-interspeech-2019/
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions
https://research.fb.com/publications/sequence-to-sequence-speech-recognition-with-time-depth-separable-convolutions/
Unsupervised Singing Voice Conversion
https://research.fb.com/publications/unsupervised-singing-voice-conversion/
Facebook
Facebook research at Interspeech 2019
Facebook is at Interspeech 2019! For those attending the conference in Graz, Austria this week, be sure to stop by booth F7 to connect with recruiters, researchers, and software engineers about speech research at Facebook. Learn more about Facebook Research…
The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.
code: https://github.com/salesforce/ctrl
article: https://einstein.ai/presentations/ctrl.pdf
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/
code: https://github.com/salesforce/ctrl
article: https://einstein.ai/presentations/ctrl.pdf
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/
GitHub
GitHub - salesforce/ctrl: Conditional Transformer Language Model for Controllable Generation
Conditional Transformer Language Model for Controllable Generation - salesforce/ctrl
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
https://arxiv.org/abs/1909.03186v1
https://arxiv.org/abs/1909.03186v1
arXiv.org
On Extractive and Abstractive Neural Document Summarization with...
We present a method to produce abstractive summaries of long documents that
exceed several thousand words via neural abstractive summarization. We perform
a simple extractive step before...
exceed several thousand words via neural abstractive summarization. We perform
a simple extractive step before...
This AI Clears Up Your Hazy Photos
Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors
article: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP/
code: https://github.com/yossigandelsman/DoubleDIP
video: https://www.youtube.com/watch?v=qkHK1QdQ2Fk
Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors
article: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP/
code: https://github.com/yossigandelsman/DoubleDIP
video: https://www.youtube.com/watch?v=qkHK1QdQ2Fk
GitHub
GitHub - yossigandelsman/DoubleDIP: Official implementation of the paper "Double-DIP: Unsupervised Image Decomposition via Coupled…
Official implementation of the paper "Double-DIP: Unsupervised Image Decomposition via Coupled Deep-Image-Priors" - yossigandelsman/DoubleDIP
A Gentle Introduction to Probability Distributions
https://machinelearningmastery.com/what-are-probability-distributions/
https://machinelearningmastery.com/what-are-probability-distributions/
NVIDIA Announces TensorRT 6; Breaks 10 millisecond barrier for BERT-Large
https://news.developer.nvidia.com/tensorrt6-breaks-bert-record/
https://news.developer.nvidia.com/tensorrt6-breaks-bert-record/
NVIDIA Technical Blog
NVIDIA Announces TensorRT 6; Breaks 10 millisecond barrier for BERT-Large | NVIDIA Technical Blog
Today, NVIDIA released TensorRT 6 which includes new capabilities that dramatically accelerate conversational AI applications, speech recognition, 3D image segmentation for medical applications…
🔍 DeepPavlov: An open-source library for end-to-end dialogue systems and chatbots
article: https://medium.com/tensorflow/deeppavlov-an-open-source-library-for-end-to-end-dialog-systems-and-chatbots-31cf26849e37
research: https://colab.research.google.com/github/deepmipt/dp_notebooks/blob/master/DP_tf.ipynb
code: https://github.com/deepmipt/DeepPavlov
article: https://medium.com/tensorflow/deeppavlov-an-open-source-library-for-end-to-end-dialog-systems-and-chatbots-31cf26849e37
research: https://colab.research.google.com/github/deepmipt/dp_notebooks/blob/master/DP_tf.ipynb
code: https://github.com/deepmipt/DeepPavlov
Medium
DeepPavlov: an open-source library for end-to-end dialog systems and chatbots
A guest post by Vasily Konovalov
🎲 Discrete Probability Distributions for Machine Learning
https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
🗣 Using AI-generated questions to train NLP systems
https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/
code: https://github.com/facebookresearch/UnsupervisedQA
paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/
https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/
code: https://github.com/facebookresearch/UnsupervisedQA
paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/
Facebook
Research in Brief: Unsupervised Question Answering by Cloze Translation
Facebook AI is releasing code for a self-supervised technique that uses AI-generated questions to train NLP systems, avoiding the need for labeled question answering training data.