Feature Engineering
The most effective way to improve your models
https://www.kaggle.com/learn/feature-engineering
The most effective way to improve your models
https://www.kaggle.com/learn/feature-engineering
Kaggle
Learn Feature Engineering Tutorials
Better features make better models. Discover how to get the most out of your data.
DeepFake Detector AIs Are Good Too!
https://www.youtube.com/watch?v=RoGHVI-w9bE
article: https://www.niessnerlab.org/projects/roessler2019faceforensicspp.html
https://www.youtube.com/watch?v=RoGHVI-w9bE
article: https://www.niessnerlab.org/projects/roessler2019faceforensicspp.html
YouTube
DeepFake Detector AIs Are Good Too!
❤️ Pick up cool perks on our Patreon page: https://www.patreon.com/TwoMinutePapers
📝 The paper "FaceForensics++: Learning to Detect Manipulated Facial Images" is available here:
https://www.niessnerlab.org/projects/roessler2019faceforensicspp.html
❤️ Watch…
📝 The paper "FaceForensics++: Learning to Detect Manipulated Facial Images" is available here:
https://www.niessnerlab.org/projects/roessler2019faceforensicspp.html
❤️ Watch…
OpenAI Plays Hide and Seek…and Breaks The Game! 🤖
https://www.youtube.com/watch?v=Lu56xVlZ40M
article: https://openai.com/blog/emergent-tool-use/
https://www.youtube.com/watch?v=Lu56xVlZ40M
article: https://openai.com/blog/emergent-tool-use/
YouTube
OpenAI Plays Hide and Seek…and Breaks The Game! 🤖
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
❤️ Their blog post is available here: https://www.wandb.com/articles/better-paths-through-idea-space
📝 The paper "Emergent Tool Use from Multi-Agent Interaction"…
❤️ Their blog post is available here: https://www.wandb.com/articles/better-paths-through-idea-space
📝 The paper "Emergent Tool Use from Multi-Agent Interaction"…
Using videos to teach AI about objects
https://research.fb.com/publications/grounded-human-object-interaction-hotspots-from-video/
https://ai.facebook.com/blog/research-in-brief-grounded-human-object-interaction-hotspots/
article: https://research.fb.com/wp-content/uploads/2019/09/Grounded-Human-Object-Interaction-Hotspots-From-Video.pdf?
https://research.fb.com/publications/grounded-human-object-interaction-hotspots-from-video/
https://ai.facebook.com/blog/research-in-brief-grounded-human-object-interaction-hotspots/
article: https://research.fb.com/wp-content/uploads/2019/09/Grounded-Human-Object-Interaction-Hotspots-From-Video.pdf?
Facebook Research
Grounded Human-Object Interaction Hotspots From Video - Facebook Research
Learning how to interact with objects is an important step towards embodied visual intelligence, but existing techniques suffer from heavy supervision or sensing requirements. We propose an approach to learn human-object interaction “hotspots” directly from…
A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
https://machinelearningmastery.com/what-is-maximum-likelihood-estimation-in-machine-learning/
https://machinelearningmastery.com/what-is-maximum-likelihood-estimation-in-machine-learning/
GPyTorch
Gaussian processes for modern machine learning systems.
https://gpytorch.ai
code: https://github.com/cornellius-gp/gpytorch
Gaussian processes for modern machine learning systems.
https://gpytorch.ai
code: https://github.com/cornellius-gp/gpytorch
GitHub
GitHub - cornellius-gp/gpytorch: A highly efficient implementation of Gaussian Processes in PyTorch
A highly efficient implementation of Gaussian Processes in PyTorch - cornellius-gp/gpytorch
Stuart_Russell___Human_Compatibl.epub
9.7 MB
Human Compatible: Artificial Intelligence and the Problem of Control
Stuart Russell
Stuart Russell
A New Workflow for Collaborative Machine Learning Research in Biodiversity
https://ai.googleblog.com/2019/10/a-new-workflow-for-collaborative.html
https://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/
MachineLearningMastery.com
A Gentle Introduction to Logistic Regression With Maximum Likelihood Estimation - MachineLearningMastery.com
Logistic regression is a model for binary classification predictive modeling. The parameters of a logistic regression model can be estimated by the probabilistic framework called maximum likelihood estimation. Under this framework, a probability distribution…
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
https://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
https://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
https://ai.googleblog.com/2019/10/learning-to-assemble-and-to-generalize.html
https://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: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Article: https://arxiv.org/pdf/1904.01326.pdf
Code: https://github.com/thunguyenphuoc/HoloGAN
Dataset: https://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.