A New Workflow for Collaborative Machine Learning Research in Biodiversity
http://ai.googleblog.com/2019/10/a-new-workflow-for-collaborative.html
http://ai.googleblog.com/2019/10/a-new-workflow-for-collaborative.html
Googleblog
A New Workflow for Collaborative Machine Learning Research in Biodiversity
Facebook research being presented at ICCV
https://ai.facebook.com/blog/facebook-research-at-iccv-2019/
https://ai.facebook.com/blog/facebook-research-at-iccv-2019/
Facebook
Facebook research being presented at ICCV
Facebook researchers will join computer vision experts from around the world to discuss the latest advances at the International Conference on Computer Vision (ICCV) in Seoul, Korea, from October 27 to November 2.
A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation
https://machinelearningmastery.com/logistic-regression-with-maximum-likelihood-estimation/
https://machinelearningmastery.com/logistic-regression-with-maximum-likelihood-estimation/
A new dense, sliding-window technique for instance segmentation
https://ai.facebook.com/blog/a-new-dense-sliding-window-technique-for-instance-segmentation/
https://ai.facebook.com/blog/a-new-dense-sliding-window-technique-for-instance-segmentation/
Meta
A new dense, sliding-window technique for instance segmentation
We’re introducing a new method that uses dense, sliding-window technique — instead of standard bounding boxes — to perform instance segmentation. .
AI Learns To Compute Game Physics In Microseconds ⚛️
https://www.youtube.com/watch?v=atcKO15YVD8
Their blog post and their CodeSearchNet system are available here:
https://www.wandb.com/articles/codesearchnet
https://app.wandb.ai/github/CodeSearchNet/benchmark
📝 The paper "Subspace Neural Physics: Fast Data-Driven Interactive Simulation" is available here:
https://static-wordpress.akamaized.net/montreal.ubisoft.com/wp-content/uploads/2019/08/27140237/deep-cloth-paper.pdf
http://theorangeduck.com/page/subspace-neural-physics-fast-data-driven-interactive-simulation
https://www.youtube.com/watch?v=atcKO15YVD8
Their blog post and their CodeSearchNet system are available here:
https://www.wandb.com/articles/codesearchnet
https://app.wandb.ai/github/CodeSearchNet/benchmark
📝 The paper "Subspace Neural Physics: Fast Data-Driven Interactive Simulation" is available here:
https://static-wordpress.akamaized.net/montreal.ubisoft.com/wp-content/uploads/2019/08/27140237/deep-cloth-paper.pdf
http://theorangeduck.com/page/subspace-neural-physics-fast-data-driven-interactive-simulation
YouTube
Ubisoft's AI Learns To Compute Game Physics In Microseconds! ⚛️
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
Their blog post and their CodeSearchNet system are available here:
https://www.wandb.com/articles/codesearchnet
https://app.wandb.ai/github/CodeSearchNet/benchmark…
Their blog post and their CodeSearchNet system are available here:
https://www.wandb.com/articles/codesearchnet
https://app.wandb.ai/github/CodeSearchNet/benchmark…
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
article: https://www.nature.com/articles/s41586-019-1724-z
https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning
article: https://www.nature.com/articles/s41586-019-1724-z
Deepmind
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
AlphaStar is the first AI to reach the top league of a widely popular esport without any game restrictions. This January, a preliminary version of AlphaStar challenged two of the world's top players in StarCraft II, one of the most enduring and popular real…
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
https://learningtopredict.github.io
https://arxiv.org/abs/1910.13038
@ArtificialIntelligencedl
https://learningtopredict.github.io
https://arxiv.org/abs/1910.13038
@ArtificialIntelligencedl
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Learning to Predict Without Looking Ahead
World Models Without Forward Prediction
Learning to Assemble and to Generalize from Self-Supervised Disassembly
http://ai.googleblog.com/2019/10/learning-to-assemble-and-to-generalize.html
http://ai.googleblog.com/2019/10/learning-to-assemble-and-to-generalize.html
Googleblog
Learning to Assemble and to Generalize from Self-Supervised Disassembly
Introducing the Temporal data set, a benchmark for recognizing actions in videos
https://ai.facebook.com/blog/introducing-the-temporal-data-set-a-benchmark-for-recognizing-actions-in-videos/
paper: https://arxiv.org/abs/1907.08340
https://ai.facebook.com/blog/introducing-the-temporal-data-set-a-benchmark-for-recognizing-actions-in-videos/
paper: https://arxiv.org/abs/1907.08340
Meta
Introducing the Temporal dataset, a benchmark for recognizing actions in videos
Facebook AI is sharing a new dataset to enable systems to better understand actions in videos — specifically those that are recognizable in video sequences but not in a single frame.
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
Learning Transferable Graph Exploration
https://arxiv.org/pdf/1910.12980.pdf
Must-read papers and continuous track on Graph Neural Network(GNN) progress
https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
https://arxiv.org/pdf/1910.12980.pdf
Must-read papers and continuous track on Graph Neural Network(GNN) progress
https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
HoloGAN (A new generative model) learns 3D representation from natural images
Article: https://arxiv.org/pdf/1904.01326.pdf
Code: https://github.com/thunguyenphuoc/HoloGAN
Dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Article: https://arxiv.org/pdf/1904.01326.pdf
Code: https://github.com/thunguyenphuoc/HoloGAN
Dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
GitHub
GitHub - thunguyenphuoc/HoloGAN: HoloGAN
HoloGAN. Contribute to thunguyenphuoc/HoloGAN development by creating an account on GitHub.
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/FriedRonaldo/SinGAN
Pytorch implementation of "SinGAN: Learning a Generative Model from a Single Natural Image»
https://arxiv.org/abs/1905.01164
https://github.com/FriedRonaldo/SinGAN
Pytorch implementation of "SinGAN: Learning a Generative Model from a Single Natural Image»
https://arxiv.org/abs/1905.01164
GitHub
GitHub - FriedRonaldo/SinGAN: Pytorch implementation of "SinGAN: Learning a Generative Model from a Single Natural Image"
Pytorch implementation of "SinGAN: Learning a Generative Model from a Single Natural Image" - FriedRonaldo/SinGAN
The latest news from Google AI
Highlights from the 2019 Google AI Residency Program
http://ai.googleblog.com/2019/11/highlights-from-2019-google-ai.html
Highlights from the 2019 Google AI Residency Program
http://ai.googleblog.com/2019/11/highlights-from-2019-google-ai.html
Google AI Blog
Highlights from the 3rd Cohort of the Google AI Residency Program
Posted by Katie Meckley, Program Manager, Google AI Residency This fall marks the successful conclusion for the third cohort of the Goog...
Fruit Identification using Arduino and TensorFlow
https://blog.tensorflow.org/2019/11/fruit-identification-using-arduino-and-tensorflow.html
https://blog.tensorflow.org/2019/11/fruit-identification-using-arduino-and-tensorflow.html
blog.tensorflow.org
Fruit Identification using Arduino and TensorFlow Lite Micro
Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple…
NVIDIA Shows Its Prowess in First AI Inference Benchmarks
https://blogs.nvidia.com/blog/2019/11/06/ai-inference-mlperf-benchmarks/
https://blogs.nvidia.com/blog/2019/11/06/ai-inference-mlperf-benchmarks/
The Official NVIDIA Blog
NVIDIA Turing, Xavier Lead in MLPerf AI Inference Benchmarks | NVIDIA Blog
MLPerf's new AI inference benchmarks gave top marks to NVIDIA Turing GPUs and Xavier SoCs.
News classification using classic Machine Learning tools (TF-IDF) and modern NLP approach based on transfer learning (ULMFIT) deployed on GCP
https://nlp.imadelhanafi.com/
code: https://github.com/imadelh/NLP-news-classification
post: https://imadelhanafi.com/posts/text_classification_ul..
https://nlp.imadelhanafi.com/
code: https://github.com/imadelh/NLP-news-classification
post: https://imadelhanafi.com/posts/text_classification_ul..
GitHub
GitHub - imadelh/NLP-news-classification: Train and deploy a News Classifier using language model (ULMFit) - Serverless container
Train and deploy a News Classifier using language model (ULMFit) - Serverless container - GitHub - imadelh/NLP-news-classification: Train and deploy a News Classifier using language model (ULMFit) ...
Auptimizer: A faster, easier way to do hyperparameter optimization for machine learning
https://github.com/LGE-ARC-AdvancedAI/auptimizer
https://towardsdatascience.com/auptimizer-a-faster-easier-way-to-do-hyperparameter-optimization-for-machine-learning-88f37c1fcfb7
https://github.com/LGE-ARC-AdvancedAI/auptimizer
https://towardsdatascience.com/auptimizer-a-faster-easier-way-to-do-hyperparameter-optimization-for-machine-learning-88f37c1fcfb7
GitHub
GitHub - LGE-ARC-AdvancedAI/auptimizer: An automatic ML model optimization tool.
An automatic ML model optimization tool. Contribute to LGE-ARC-AdvancedAI/auptimizer development by creating an account on GitHub.
This AI Clones Your Voice After Listening for 5 Seconds 🤐
https://www.youtube.com/watch?v=0sR1rU3gLzQ
📝 The paper "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis" and audio samples are available here:
https://arxiv.org/abs/1806.04558
https://google.github.io/tacotron/publications/speaker_adaptation
https://www.youtube.com/watch?v=0sR1rU3gLzQ
📝 The paper "Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis" and audio samples are available here:
https://arxiv.org/abs/1806.04558
https://google.github.io/tacotron/publications/speaker_adaptation
YouTube
Google's AI Clones Your Voice After Listening for 5 Seconds! 🤐
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
The shown blog post is available here: https://www.wandb.com/articles/fundamentals-of-neural-networks
📝 The paper "Transfer Learning from Speaker Verification…
The shown blog post is available here: https://www.wandb.com/articles/fundamentals-of-neural-networks
📝 The paper "Transfer Learning from Speaker Verification…