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Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs

Neural Information Processing Systems

We propose a simple interpolation-based method for the efficient approximation of gradients in neural ODE models. We compare it with reverse dynamic method (known in literature as "adjoint method") to train neural ODEs on classification, density estimation and inference approximation tasks. We also propose a theoretical justification of our approach using logarithmic norm formalism. As a result, our method allows faster model training than the reverse dynamic method what was confirmed and validated by extensive numerical experiments for several standard benchmarks.


New Technique Significantly Speeds Up Deep Learning On Large Graphs

#artificialintelligence

"We started to look at the challenges current systems experienced when scaling state-of-the-art machine learning techniques for graphs to really …


3 Techniques To Speed Up Data Annotation - Big Data Analytics News

#artificialintelligence

Computer vision can easily distinguish between well-defined shapes, for instance, a sphere and a cube. Things go awry with less distinct forms. It's easy for the human eye to differentiate between a cat and a dog -- you know what is what. But computers have no such innate capability, and even the most advanced computer vision algorithms often mistake a cat for a dog and vice versa. Computers have to be trained rigorously to classify fuzzy objects.


Health care innovation moving at 'speed of light'

#artificialintelligence

"Something that is quite interesting is deep learning (or) artificial intelligence that can gather through data from different sources, images, diagnostic …


9 Artificial Intelligence Startups in Lebanon - Nanalyze

#artificialintelligence

With roughly the same population as the State of Missouri, Lebanon is a small country of six million people that borders Syria and Israel. Due to its location, the country has been subjected to a multitude of political and religious factions inhabiting the state. People frequently fight over whose invisible friend is better, and the country has faced long periods of instability including wars with Israel, civil wars and internal conflicts, and most recently some spillover from the Syrian war – which means lots of Syrians flying around on motorcycles. All of this turmoil has contributed to structural problems in the economy such as chronic fiscal deficits that have increased Lebanon's debt-to-GDP ratio to the third highest in the world. Economic growth has slowed to 1-2% over the past decade which constrains government investments in necessary infrastructure improvements. Notwithstanding these challenges, day to day life in Lebanon is pretty awesome.


Tuning Random Forest model Machine Learning Predictive modeling

@machinelearnbot

A month back, I participated in a Kaggle competition called TFI. I started with my first submission at 50th percentile. Having worked relentlessly on feature engineering for more than 2 weeks, I managed to reach 20th percentile. To my surprise, right after tuning the parameters of the machine learning algorithm I was using, I was able to breach top 10th percentile. This is how important tuning these machine learning algorithms are.