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ICLR 2020: Yoshua Bengio and the Nature of Consciousness

Machine Learning Street Talk (MLST)

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Navigating Sparse Representations in Neural Networks

This chapter explores the intricate challenges of learning sparse representations in machine learning, particularly the limitations of fully connected networks. It highlights the complexities of balancing dense and sparse connections and the implications for effective learning and growth within neural models. The discussion also introduces evolutionary techniques as a means to enhance research collaboration and transparency while addressing the difficulties of connecting concepts meaningfully.

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