Ruslan Salakhutdinov
Known for his significant contributions to deep learning, probabilistic graphical models, and large-scale optimisation, Ruslan Salakhutdinov received his PhD in machine learning from the University of Toronto in 2009, under the supervision of Geoffrey Hinton. This period marked a pivotal moment in his career, as he was part of the team that developed “deep belief networks”, a breakthrough in deep learning. Salakhutdinov has made substantial contributions to deep learning, particularly in the development of deep belief networks (DBNs) and other neural network architectures. His work on DBNs, published in 2006, was instrumental in advancing the field of deep learning. He has also worked extensively on probabilistic graphical models, which are crucial for understanding complex systems and making predictions in various domains. Salakhutdinov also co-developed the dropout algorithm, a technique used to prevent neural networks from overfitting, which has become a standard tool in deep learning. Salakhutdinov is a professor of computer science in the machine learning department at Carnegie Mellon University. He has also held positions at the University of Toronto and was a director of AI research at Apple from 2016 to 2020.
UPMC Professor, Machine Learning
Find out why Ruslan Salakhutdinov, UPMC Professor, Machine Learning is in the AIM 100 list as top AI innovators.
["AI and machine learning"]
["Go", "machine learning", "programming_languages:R", "AI", "neural network", "programming_languages:Go", "deep learning", "AI and machine learning", "AI research", "R"]
["AI", "machine learning", "deep learning", "neural network", "R", "Go", "AI research", "programming_languages:R", "programming_languages:Go"]