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Most deep learning networks today rely on dense representations. This is in stark contrast to our brains which are extremely sparse.
This session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.
This session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
In this talk, Subutai will first discuss what is known about the sparsity of activations and connectivity in the neocortex. He will also summarize new experimental data around active dendrites, branch-specific plasticity, and structural plasticity, each of which has surprising implications for how we think about sparsity. In the second half of the talk, Subutai will discuss how these insights from the brain can be applied to practical machine learning applications. He will show how sparse representations can give rise to improved robustness, continuous learning, powerful unsupervised learning rules, and improved computational efficiency.