Video Architecture Search
http://ai.googleblog.com/2019/10/video-architecture-search.html
Evolving Space-Time Neural Architectures for Videos
https://github.com/google-research/google-research/tree/master/evanet
Tiny Video Networks
https://arxiv.org/abs/1910.06961
http://ai.googleblog.com/2019/10/video-architecture-search.html
Evolving Space-Time Neural Architectures for Videos
https://github.com/google-research/google-research/tree/master/evanet
Tiny Video Networks
https://arxiv.org/abs/1910.06961
Googleblog
Video Architecture Search
Naive Bayes Classifier From Scratch in Python
https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
Benchmarking Transformers: PyTorch and TensorFlow
https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2
https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2
Medium
Benchmarking Transformers: PyTorch and TensorFlow
Our Transformers library implements several state-of-the-art transformer architectures used for NLP tasks like text classification…
A Gentle Introduction to Cross-Entropy for Machine Learning
https://machinelearningmastery.com/cross-entropy-for-machine-learning/
https://machinelearningmastery.com/cross-entropy-for-machine-learning/
Graph Representation Learning
https://grlearning.github.io/papers/
Awesome Graph Classification
https://github.com/benedekrozemberczki/awesome-graph-classification
https://grlearning.github.io/papers/
Awesome Graph Classification
https://github.com/benedekrozemberczki/awesome-graph-classification
grlearning.github.io
Accepted Papers
NeurIPS 2019 Workshop
Audio and Visual Quality Measurement using Fréchet Distance
http://ai.googleblog.com/2019/10/audio-and-visual-quality-measurement.html
http://ai.googleblog.com/2019/10/audio-and-visual-quality-measurement.html
research.google
Audio and Visual Quality Measurement Using Fréchet Distance
Posted by Kevin Kilgour, Software Engineer and Thomas Unterthiner, Research Software Engineer, Google Research, Zürich "I often say that when you...
Deepfake Detection Challenge: AWS and new academics join, initial data set released
https://ai.facebook.com/blog/deepfake-detection-challenge-aws-and-new-academics-join/
The Deepfake Detection Challenge (DFDC) Preview Dataset
https://arxiv.org/abs/1910.08854?fbclid=IwAR2F6Z-BVoxoc9TlhUYIx-yMkMF9XzkbWj38Hj0HYK5tFJ6zYNXfwUzsP6w
https://deepfakedetectionchallenge.ai/?fbclid=IwAR0b0AhrWFUMzwOaZuZMhv8UVrCu7Jcfj5_I66rwvL54089bI4ysNNYWbz4
https://ai.facebook.com/blog/deepfake-detection-challenge-aws-and-new-academics-join/
The Deepfake Detection Challenge (DFDC) Preview Dataset
https://arxiv.org/abs/1910.08854?fbclid=IwAR2F6Z-BVoxoc9TlhUYIx-yMkMF9XzkbWj38Hj0HYK5tFJ6zYNXfwUzsP6w
https://deepfakedetectionchallenge.ai/?fbclid=IwAR0b0AhrWFUMzwOaZuZMhv8UVrCu7Jcfj5_I66rwvL54089bI4ysNNYWbz4
Facebook
Deepfake Detection Challenge: AWS and new academics join, initial dataset released
The Deepfake Detection Challenge is adding new partners, including Amazon Web Services, and also sharing an initial dataset of videos.
Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Molecules
https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
https://arxiv.org/abs/1910.10685
https://ai.googleblog.com/2019/10/learning-to-smell-using-deep-learning.html
Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules
https://arxiv.org/abs/1910.10685
research.google
Learning to Smell: Using Deep Learning to Predict the Olfactory Properties of Mo
Posted by Alexander B Wiltschko, Senior Research Scientist, Google Research Smell is a sense shared by an incredible range of living organisms, a...
ICCV 2019 papers open access
http://openaccess.thecvf.com/ICCV2019.py
Workshops:
http://openaccess.thecvf.com/ICCV2019_workshops/menu.py
http://openaccess.thecvf.com/ICCV2019.py
Workshops:
http://openaccess.thecvf.com/ICCV2019_workshops/menu.py
A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation
https://machinelearningmastery.com/linear-regression-with-maximum-likelihood-estimation/
https://machinelearningmastery.com/linear-regression-with-maximum-likelihood-estimation/
Build and deploy TensorFlow.js models with the power of AutoML
https://medium.com/tensorflow/build-and-deploy-tensorflow-js-models-with-the-power-of-automl-1985b8985083
Edge TensorFlow.js tutorial
https://cloud.google.com/vision/automl/docs/tensorflow-js-tutorial
AutoML Edge API
https://www.npmjs.com/package/@tensorflow/tfjs-automl
https://medium.com/tensorflow/build-and-deploy-tensorflow-js-models-with-the-power-of-automl-1985b8985083
Edge TensorFlow.js tutorial
https://cloud.google.com/vision/automl/docs/tensorflow-js-tutorial
AutoML Edge API
https://www.npmjs.com/package/@tensorflow/tfjs-automl
Medium
Build and deploy TensorFlow.js models with the power of AutoML
Posted by Daniel Smilkov, Sandeep Gupta, and Vishy Tirumalashetty
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
https://github.com/flowersteam/teachDeepRL
https://github.com/flowersteam/teachDeepRL
GitHub
GitHub - flowersteam/teachDeepRL
Contribute to flowersteam/teachDeepRL development by creating an account on GitHub.
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
https://meta-world.github.io/
article: https://arxiv.org/abs/1910.10897v1
github: https://github.com/rlworkgroup/metaworld
https://meta-world.github.io/
article: https://arxiv.org/abs/1910.10897v1
github: https://github.com/rlworkgroup/metaworld
arXiv.org
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta...
Meta-reinforcement learning algorithms can enable robots to acquire new
skills much more quickly, by leveraging prior experience to learn how to learn.
However, much of the current research on...
skills much more quickly, by leveraging prior experience to learn how to learn.
However, much of the current research on...
Open-sourcing SlowFast, competition-leading video recognition through dual frame-rate analysis
https://ai.facebook.com/blog/slowfast-video-recognition-through-dual-frame-rate-analysis/
github: https://github.com/facebookresearch/SlowFast
paper: https://arxiv.org/abs/1812.03982
https://ai.facebook.com/blog/slowfast-video-recognition-through-dual-frame-rate-analysis/
github: https://github.com/facebookresearch/SlowFast
paper: https://arxiv.org/abs/1812.03982
Facebook
Open-sourcing SlowFast, competition-leading video recognition through dual frame-rate analysis
SlowFast is a new, open source video recognition technique that uses separate pathways to look for spatial features (such as colors and textures) and temporal features (such as rapid motion).
Deep Learning Drizzle
code: https://github.com/kmario23/deep-learning-drizzle
https://deep-learning-drizzle.github.io
code: https://github.com/kmario23/deep-learning-drizzle
https://deep-learning-drizzle.github.io
GitHub
GitHub - kmario23/deep-learning-drizzle: Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision…
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!! - kmario23/deep-learning-drizzle
Introducing the Schema-Guided Dialogue Dataset for Conversational Assistants
http://ai.googleblog.com/2019/10/introducing-schema-guided-dialogue.html
Schema-Guided Dialogue State Tracking
https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
article : https://arxiv.org/pdf/1909.05855.pdf
http://ai.googleblog.com/2019/10/introducing-schema-guided-dialogue.html
Schema-Guided Dialogue State Tracking
https://github.com/google-research-datasets/dstc8-schema-guided-dialogue
article : https://arxiv.org/pdf/1909.05855.pdf
research.google
Introducing the Schema-Guided Dialogue Dataset for Conversational Assistants
Posted by Abhinav Rastogi, Software Engineer and Pranav Khaitan, Engineering Lead, Google Research Today's virtual assistants help users to accom...
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
https://arxiv.org/abs/1906.12340v2
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
https://github.com/hendrycks/ss-ood
Download the one class ImageNet test set here:
https://drive.google.com/file/d/13xzVuQMEhSnBRZr-YaaO08coLU2dxAUq/view
https://arxiv.org/abs/1906.12340v2
Self-Supervised Learning for OOD Detection (NeurIPS 2019)
https://github.com/hendrycks/ss-ood
Download the one class ImageNet test set here:
https://drive.google.com/file/d/13xzVuQMEhSnBRZr-YaaO08coLU2dxAUq/view
arXiv.org
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty
Self-supervision provides effective representations for downstream tasks
without requiring labels. However, existing approaches lag behind fully
supervised training and are often not thought...
without requiring labels. However, existing approaches lag behind fully
supervised training and are often not thought...
Hugging Face: State-of-the-Art Natural Language Processing in ten lines of TensorFlow 2.0
https://medium.com/tensorflow/using-tensorflow-2-for-state-of-the-art-natural-language-processing-102445cda54a
code: https://github.com/huggingface/transformers
https://medium.com/tensorflow/using-tensorflow-2-for-state-of-the-art-natural-language-processing-102445cda54a
code: https://github.com/huggingface/transformers
Medium
Using TensorFlow 2 for State-of-the-Art Natural Language Processing
A guest post by Thomas Wolf and Lysandre Debut from Hugging Face. All examples used in this tutorial are available on Colab. The links are…
A Gentle Introduction to Expectation-Maximization (EM Algorithm)
https://machinelearningmastery.com/expectation-maximization-em-algorithm/
https://machinelearningmastery.com/expectation-maximization-em-algorithm/
MachineLearningMastery.com
A Gentle Introduction to Expectation-Maximization (EM Algorithm) - MachineLearningMastery.com
Maximum likelihood estimation is an approach to density estimation for a dataset by searching across probability distributions and their parameters. It is a general and effective approach that underlies many machine learning algorithms, although it requires…