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…
Visual Wake Words with TensorFlow Lite Micro
https://medium.com/tensorflow/visual-wake-words-with-tensorflow-lite-micro-8578e59ea6f9
dataset: https://github.com/tensorflow/models/blob/master/research/slim/datasets/build_visualwakewords_data.py
tutorial: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/person_detection/training_a_model.md
https://medium.com/tensorflow/visual-wake-words-with-tensorflow-lite-micro-8578e59ea6f9
dataset: https://github.com/tensorflow/models/blob/master/research/slim/datasets/build_visualwakewords_data.py
tutorial: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/person_detection/training_a_model.md
Medium
Visual Wake Words with TensorFlow Lite Micro
Posted by Aakanksha Chowdhery, Software Engineer
Forwarded from Artificial Intelligence
Hamiltonian Neural Networks
https://eng.uber.com/research/hamiltonian-neural-networks/
paper: https://arxiv.org/pdf/1906.01563.pdf
code: https://github.com/greydanus/hamiltonian-nn
https://eng.uber.com/research/hamiltonian-neural-networks/
paper: https://arxiv.org/pdf/1906.01563.pdf
code: https://github.com/greydanus/hamiltonian-nn
A Gentle Introduction to Monte Carlo Sampling for Probability
https://machinelearningmastery.com/monte-carlo-sampling-for-probability/
https://machinelearningmastery.com/monte-carlo-sampling-for-probability/
A minimalist neural machine translation toolkit based on PyTorch that is specifically designed for novices.
https://arxiv.org/abs/1907.12484
https://github.com/joeynmt/joeynmt
https://arxiv.org/abs/1907.12484
https://github.com/joeynmt/joeynmt
arXiv.org
Joey NMT: A Minimalist NMT Toolkit for Novices
We present Joey NMT, a minimalist neural machine translation toolkit based on
PyTorch that is specifically designed for novices. Joey NMT provides many
popular NMT features in a small and simple...
PyTorch that is specifically designed for novices. Joey NMT provides many
popular NMT features in a small and simple...
The Visual Task Adaptation Benchmark
http://ai.googleblog.com/2019/11/the-visual-task-adaptation-benchmark.html
http://ai.googleblog.com/2019/11/the-visual-task-adaptation-benchmark.html
Googleblog
The Visual Task Adaptation Benchmark
Abstraction and Reasoning Corpus
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
Paper: https://arxiv.org/abs/1911.01547
ARC: https://github.com/fchollet/ARC
arXiv.org
On the Measure of Intelligence
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate...
This project is adapted from the original Dive Into Deep Learning book
https://github.com/dsgiitr/d2l-pytorch
https://github.com/dsgiitr/d2l-pytorch
GitHub
GitHub - dsgiitr/d2l-pytorch: This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from…
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch. - dsgiitr/d2l-pytorch