Summary of famous machine learning patents
Machine learning community is relatively open. Papers can be dowloaded from arXiv for free. On the other hand, we sometimes found some methods are registered as patents.
I daily collect these patents shared on Twitter and Reddit. The patents below are the list of machine learning patents. Some patents are under application, not approved.
Generating output sequences from input sequences using neural networks by Google
known as seq2seq
System and method for addressing overfitting in a neural network by Google
known as Dropout
Batch normalization layers
As title shows, Batch Normalization patents. According to this slide, the patents are registered in many countries.
Object detection and classification in images by Microsoft
Faster R-CNN (object detection). According to qiita article, it is only applied to U.S.
DATA AUGMENTATION FOR IMAGE CLASSIFICATION TASKS by IBM
similar to Mixup
Spectrogram to waveform synthesis using convolutional networks by Baidu
waveform generation from spectrogram by (Multi-head) CNN
Machine learning to generate music from text by Google
Music Generation from text
Deep reinforcement learning for robotic manipulation by Google
Multiple robots learn individually. By using acquired experiments, policy network is learned.
Active Machine Learning by Microsoft
When new labels are added, model start relearning in the limit of model size.
If it is paper, the explicit description like xx% precision improvement is popular. The patents description is ambiguous for enlarging the claim rights.
If you know the other patens, please let me know.