Microsoft Patents
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Advancements In Training Neural Networks
Microsoft filed these patents around Advancements In Training Neural Networks in the last 5 years
| # | Patent No. | Short Description |
|---|---|---|
| 1. | US20190266246A1 | Modeling sequences using segmentations to improve output predictions |
| 2. | US20230267319A1 | Training and operating neural networks using mixed precision floating point formats to reduce memory and computational requirements |
| 3. | US11657799B2 | Training techniques for recurrent neural network transducers (RNN-T) to improve accuracy and reduce training time |
| 4. | US20210174146A1 | Improving an object recognition system by suggesting additional training images that would improve the system's performance |
| 5. | US11625627B2 | A deep learning framework for microclimate prediction that uses a combination of techniques to improve accuracy and adaptability |
| 6. | US10460234B2 | Securely training deep neural networks using private data from multiple parties without sharing the data |
| 7. | US11809909B2 | Providing synthetic data as a service (SDaaS) to democratize and democratize training datasets for machine learning |
| 8. | US20230196085A1 | Training neural networks using quantized floating-point formats to improve performance on low-power hardware like FPGAs |
| 9. | US20200265301A1 | Incremental training of a machine learning model using unsupervised data |
| 10. | US20200210840A1 | Training neural networks using quantized floating-point representations to accelerate training and inference |
| 11. | US11741362B2 | Training neural networks using mixed-precision computations to reduce training time and resources |
| 12. | US10679610B2 | Eyes-off training of a dictation system using corrections supplied by a user to improve the system's accuracy |
| 13. | US20230186094A1 | Probabilistic neural network architecture generation using Monte Carlo methods to avoid fully training intermediate architectures or evaluating the complete search graph |
| 14. | US11636389B2 | Improving machine learning models for categorizing data by identifying and removing inaccurate training samples to improve the accuracy of the model |
| 15. | US11586930B2 | Conditional teacher-student learning for improving the performance of student models trained using teacher models |
Download the full patent report containing all of the 172 patents | ||
