Researchers have improved the widely used deep learning optimizer Adam, decreasing the time needed for training models and improving model generalization. We made a small contribution of our own by open sourcing our PyTorch implementation of QHAdamW.
The story of our first steps towards explainable patent search. In the end of the post you get to test our unique approach, which is one of the first few serious attempts to provide real world value from explainable NLP deep learning models.
IPRally's knowledge graph based patent search is now free to try out.
Knowledge graphs are a great fit for patents. The benefits of a graph based patent search will be increasingly hard to compete with the traditional approaches.
IPRally won the most innovative AI startup award in North Star AI conference. The event had some star speakers like Estonian president Kersti Kaljulaid and DeepMind's Samuel Ritter.
The most notable IPR innovation award of Finland, granted by the IPR University Center, was given to IPRally.
Our story about utilising neural nets with tree like data. Can a Tree-LSTM model be used for real world applications? Turns out the answer is yes, but only after the performance is improved by 7000%.
The story of IPRally in a nutshell. From initial questions to validation of ideas and a working product.
Is your organization willing to be in the IPR frontline? Get in touch to get a demo or take a sneak peek of the future of patent AI as we see it.
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