Neural Arithmetic Units & Experiences as an Independent ML Researcher with Andreas Madsen - #382
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Extrapolation Challenges in Neural Networks
This chapter explores the limitations of neural networks, particularly their difficulties with extrapolation when presented with unfamiliar data modifications. The discussion highlights research aimed at enhancing neural arithmetic units to improve extrapolation capabilities and critiques existing evaluation methods in machine learning. By proposing innovative benchmarks and success criteria, the chapter aims to refine the performance measurement of these models.
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