Selective Brain Damage: Measuring the Disparate Impact of Model Compression
https://weightpruningdamage.github.io/
https://weightpruningdamage.github.io/
Deep Neural Network Pruning
Selective Brain Damage
What do pruned deep neural networks forget?
Finding label errors in datasets and learning with noisy labels.
https://github.com/cgnorthcutt/cleanlab/
https://github.com/cgnorthcutt/cleanlab/
GitHub
GitHub - cgnorthcutt/cleanlab: Official cleanlab repo is at https://github.com/cleanlab/cleanlab
Official cleanlab repo is at https://github.com/cleanlab/cleanlab - cgnorthcutt/cleanlab
Open Set Recognition Through Deep Neural Network Uncertainty
code: https://github.com/MrtnMndt/Deep_Openset_Recognition_through_Uncertainty
paper: https://openaccess.thecvf.com/content_ICCVW_2019/papers/SDL-CV/Mundt_Open_Set_Recognition_Through_Deep_Neural_Network_Uncertainty_Does_Out-of-Distribution_ICCVW_2019_paper.pdf
code: https://github.com/MrtnMndt/Deep_Openset_Recognition_through_Uncertainty
paper: https://openaccess.thecvf.com/content_ICCVW_2019/papers/SDL-CV/Mundt_Open_Set_Recognition_Through_Deep_Neural_Network_Uncertainty_Does_Out-of-Distribution_ICCVW_2019_paper.pdf
GitHub
MrtnMndt/Deep_Openset_Recognition_through_Uncertainty
PyTorch code for our paper: Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers? https://arxiv.org/abs/1908.09625 - Mrtn...
Astrophotography with Night Sight on Pixel Phones
https://ai.googleblog.com/2019/11/astrophotography-with-night-sight-on.html
https://ai.googleblog.com/2019/11/astrophotography-with-night-sight-on.html
research.google
Astrophotography with Night Sight on Pixel Phones
Posted by Florian Kainz and Kiran Murthy, Software Engineers, Google Research Taking pictures of outdoor scenes at night has so far been the dom...
Using AI to create real-time depth maps for occlusions in AR
https://ai.facebook.com/blog/using-ai-to-create-real-time-depth-maps-for-occlusions-in-ar/
https://research.fb.com/publications/fast-depth-densification-for-occlusion-aware-augmented-reality/
https://ai.facebook.com/blog/using-ai-to-create-real-time-depth-maps-for-occlusions-in-ar/
https://research.fb.com/publications/fast-depth-densification-for-occlusion-aware-augmented-reality/
Facebook
Using AI to Create Real-Time Depth Maps for Occlusions in AR
Learn how we enable AR effects to fully interact with a scene geometry, for example, to enable occlusions by real objects in the scene.
Graph Neural Ordinary Differential Equations
Extending Graph Neural Networks into a continuous depth domain
https://github.com/Zymrael/gde
https://towardsdatascience.com/graph-neural-ordinary-differential-equations-a5e44ac2b6ec
Extending Graph Neural Networks into a continuous depth domain
https://github.com/Zymrael/gde
https://towardsdatascience.com/graph-neural-ordinary-differential-equations-a5e44ac2b6ec
GitHub
GitHub - Zymrael/gde: Neural Graph Differential Equations (Neural GDEs)
Neural Graph Differential Equations (Neural GDEs). Contribute to Zymrael/gde development by creating an account on GitHub.
Autonomous Vehicle Radar Perception in 360 Degrees
https://devblogs.nvidia.com/autonomous-vehicle-radar-perception-in-360-degrees/
https://devblogs.nvidia.com/autonomous-vehicle-radar-perception-in-360-degrees/
NVIDIA Developer Blog
Autonomous Vehicle Radar Perception in 360 Degrees | NVIDIA Developer Blog
Our radar perception pipeline delivers 360-degree surround perception around the vehicle, using production-grade radar sensors operating at the 77GHz automotive microwave band.
GNNExplainer: Generating Explanations for Graph Neural Networks
https://arxiv.org/abs/1903.03894
Github : https://github.com/RexYing/gnn-model-explainer/
https://arxiv.org/abs/1903.03894
Github : https://github.com/RexYing/gnn-model-explainer/
GitHub
GitHub - RexYing/gnn-model-explainer: gnn explainer
gnn explainer. Contribute to RexYing/gnn-model-explainer development by creating an account on GitHub.
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labels
https://ai.googleblog.com/2019/12/developing-deep-learning-models-for.html
https://ai.googleblog.com/2019/12/developing-deep-learning-models-for.html
Googleblog
Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labels
Facebook's Head of AI Says the Field Will Soon ‘Hit the Wall
https://www.wired.com/story/facebooks-ai-says-field-hit-wall/
https://www.wired.com/story/facebooks-ai-says-field-hit-wall/
WIRED
Facebook's Head of AI Says the Field Will Soon ‘Hit the Wall’
Jerome Pesenti is encouraged by progress in artificial intelligence, but sees the limits of the current approach to deep learning.
How to Use Out-of-Fold Predictions in Machine Learning
https://machinelearningmastery.com/out-of-fold-predictions-in-machine-learning/
https://machinelearningmastery.com/out-of-fold-predictions-in-machine-learning/
MachineLearningMastery.com
How to Use Out-of-Fold Predictions in Machine Learning - MachineLearningMastery.com
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions…
Building AI that can master complex cooperative games with hidden information
https://ai.facebook.com/blog/building-ai-that-can-master-complex-cooperative-games-with-hidden-information/
https://ai.facebook.com/blog/building-ai-that-can-master-complex-cooperative-games-with-hidden-information/
Facebook
Building AI that can master complex cooperative games with hidden information
To advance research on AI that can understand others’ points of view and collaborate effectively, Facebook AI has developed a bot that sets a new state of the art in Hanabi, a card game in which all players work together.
A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
Article: https://arxiv.org/abs/1911.12893
Code & Dataset: https://github.com/mhagiwara/github-typo-corpus
Article: https://arxiv.org/abs/1911.12893
Code & Dataset: https://github.com/mhagiwara/github-typo-corpus
GitHub
GitHub - mhagiwara/github-typo-corpus: GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors
GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors - mhagiwara/github-typo-corpus
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
https://arxiv.org/abs/1910.06922
SIte : https://ajolicoeur.wordpress.com/
Github : https://github.com/AlexiaJM/MaximumMarginGANs
https://arxiv.org/abs/1910.06922
SIte : https://ajolicoeur.wordpress.com/
Github : https://github.com/AlexiaJM/MaximumMarginGANs
Alexia Jolicoeur-Martineau, Ph.D.
AI Researcher at the Samsung SAIT AI Lab 🐱💻
How the AI community can get serious about reproducibility
https://ai.facebook.com/blog/how-the-ai-community-can-get-serious-about-reproducibility
https://ai.facebook.com/blog/how-the-ai-community-can-get-serious-about-reproducibility
Facebook
How the AI community can get serious about reproducibility
Facebook AI Managing Director Joelle Pineau discusses the importance of reproducibility in AI research, and shares early results from the first reproducibility challenge at NeurIPS 2019.
FastSpeech: New text-to-speech model improves on speed, accuracy, and controllability
https://www.microsoft.com/en-us/research/blog/fastspeech-new-text-to-speech-model-improves-on-speed-accuracy-and-controllability/
https://www.microsoft.com/en-us/research/blog/fastspeech-new-text-to-speech-model-improves-on-speed-accuracy-and-controllability/
Microsoft Research
FastSpeech: New text-to-speech model improves on speed, accuracy, and controllability - Microsoft Research
Text to speech (TTS) has attracted a lot of attention recently due to advancements in deep learning. Neural network-based TTS models (such as Tacotron 2, DeepVoice 3 and Transformer TTS) have outperformed conventional concatenative and statistical parametric…
FastMRI initiative releases neuroimaging data set
https://ai.facebook.com/blog/fastmri-releases-neuroimaging-data-set/
https://ai.facebook.com/blog/fastmri-releases-neuroimaging-data-set/
Facebook
FastMRI initiative releases neuroimaging dataset
As part of the fastMRI research project to use AI to speed up MRI scans, NYU Langone Health is making a new dataset of de-identified brain MRIs available to researchers and Facebook AI is sharing additional tools and resources.