R&D Machine Learning Engineer and Researcher at Ironscales and independent Machine Learning Researcher at TensorCell. Before joining Ironscales Alex has built end-to-end machine learning systems for Fortune Global 100 and 500 companies.
International speaker, blogger, currently working on a book on causality in Python. Interested in NLP, causality, probabilistic modeling, representation learning and graph neural networks.
Loves traveling with his wife, passionate about vegan food, languages and running.
From summarizing distributions to pooling operators and measuring the goodness of fit, averaging plays a unique and universally recognized role in the field of machine learning. In the talk we present Generalized Average (GA) - a continuous and fully differentiable average operator that allows for flexible interpolation between min, max and three different types of averages: arithmetic, geometric and harmonic. We share the results of two lines of experiments: (1) using GA in hyperparameter tuning for false-positive-averse cases (e.g. fraud detection) and (2) using GA as a pooling operator in Graph Attention Networks to improve the model’s flexibility. Finally, we present an open-source Python package with our implementation of GA. The talk is addressed to machine learning practitioners, who are interested in enriching their toolbox.