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: https://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: https://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/
MachineLearningMastery.com
A Gentle Introduction to Probability Distributions - MachineLearningMastery.com
Probability can be used for more than calculating the likelihood of one event; it can summarize the likelihood of all possible outcomes.
A thing of interest in probability is called a random variable, and the relationship between each possible outcome…
A thing of interest in probability is called a random variable, and the relationship between each possible outcome…
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/
MachineLearningMastery.com
Discrete Probability Distributions for Machine Learning - MachineLearningMastery.com
The probability for a discrete random variable can be summarized with a discrete probability distribution.
Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems,…
Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems,…
🗣 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.
If you are programmer or a student / graduate or PHD. IF you have basic knowledge of higher mathematics, probability theory and python? If you dream to try yourself in Data Science?
MegaFon announces a competition for participation in the five-day intensive BigDataCamp!
You could become a participant in the training, just go through testing and write a motivation letter.
All details on the website: https://bigdatacamp.megafon.ru/
MegaFon announces a competition for participation in the five-day intensive BigDataCamp!
You could become a participant in the training, just go through testing and write a motivation letter.
All details on the website: https://bigdatacamp.megafon.ru/
Neural networks in NLP are vulnerable to adversarially crafted inputs.
We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:
https://arxiv.org/abs/1909.01492
We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:
https://arxiv.org/abs/1909.01492
arXiv.org
Achieving Verified Robustness to Symbol Substitutions via Interval...
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and...
Forwarded from Artificial Intelligence
Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs
code: https://github.com/nianticlabs/depth-hints
paper: https://arxiv.org/abs/1909.09051
dataset : https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
code: https://github.com/nianticlabs/depth-hints
paper: https://arxiv.org/abs/1909.09051
dataset : https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
GitHub
GitHub - nianticlabs/depth-hints: [ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation…
[ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs - nianticlabs/depth-hints
Continuous Probability Distributions for Machine Learning
https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
Torchdata is PyTorch oriented library focused on data processing and input pipelines in general
https://github.com/szymonmaszke/torchdata
https://github.com/szymonmaszke/torchdata
GitHub
GitHub - szymonmaszke/torchdatasets: PyTorch dataset extended with map, cache etc. (tensorflow.data like)
PyTorch dataset extended with map, cache etc. (tensorflow.data like) - szymonmaszke/torchdatasets
Creating a Custom TFX Executor
https://medium.com/tensorflow/creating-a-custom-tfx-executor-28edaa3604f6
The ExampleGen TFX Pipeline Component:
https://github.com/tensorflow/tfx/blob/master/docs/guide/examplegen.md#custom-examplegen
video:
https://www.youtube.com/playlist?list=PLQY2H8rRoyvxR15n04JiW0ezF5HQRs_8F
https://medium.com/tensorflow/creating-a-custom-tfx-executor-28edaa3604f6
The ExampleGen TFX Pipeline Component:
https://github.com/tensorflow/tfx/blob/master/docs/guide/examplegen.md#custom-examplegen
video:
https://www.youtube.com/playlist?list=PLQY2H8rRoyvxR15n04JiW0ezF5HQRs_8F
Medium
Creating a Custom TFX Executor
Posted by Kevin Haas, Zhitao Li, and Robert Crowe on behalf of the TFX team
Contributing Data to Deepfake Detection Research
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/ondyari/FaceForensics/
FaceForensics benchmark
https://kaldir.vc.in.tum.de/faceforensics_benchmark/
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/ondyari/FaceForensics/
FaceForensics benchmark
https://kaldir.vc.in.tum.de/faceforensics_benchmark/
research.google
Contributing Data to Deepfake Detection Research
Posted by Nick Dufour, Google Research and Andrew Gully, Jigsaw Deep learning has given rise to technologies that would have been thought impossibl...
Using TensorFlow to predict product weight and dimensions
https://medium.com/tensorflow/using-tensorflow-to-predict-product-weight-and-dimensions-8e6af3ab3466
https://medium.com/tensorflow/using-tensorflow-to-predict-product-weight-and-dimensions-8e6af3ab3466
Medium
Using TensorFlow to predict product weight and dimensions
A Guest Post by Rodolfo Bonnin from the Mercado Libre Applied Machine Learning team