Yoshua Bengio Proposes New Inductive Biases to Augment Deep Learning

Yoshua Bengio Proposes New Inductive Biases to Augment Deep Learning

Recently, two researchers from the University of Montreal, Yoshua Bengio and Anirudh Goyal proposed new inductive biases that are meant to boost the deep learning performance. This paper focuses mainly on those inductive biases that concern mostly higher-level and sequential conscious processing. To be specific, this research’s main idea is to bridge the gap between human cognitive abilities and current techniques of deep learning.

Recently, two researchers from the University of Montreal, Yoshua Bengio and Anirudh Goyal proposed new inductive biases that are meant to boost the deep learning performance. This paper focuses mainly on those inductive biases that concern mostly higher-level and sequential conscious processing. To be specific, this research’s main idea is to bridge the gap between human cognitive abilities and current techniques of deep learning. 

Although the deep learning technique has achieved various groundbreaking results, there have always been controversies around it. For instance, have the main principles needed for deep learning techniques in order to achieve human-level performance been discovered? Or do one need to pursue a completely different research direction with deep learning techniques to achieve the kind of cognitive competence displayed by humans? 

Through this research, the Canadian Computer Scientist and his team tried to understand the main gap between current deep learning and human cognitive abilities to answer these questions and suggest research directions for deep learning. The research was aimed at bridging the gap towards human-level AI.

In the present scenario, deep learning ventures leverages several key inductive biases, and the technique has achieved much accuracy across a variety of tasks and applications. Their key hypothesis was that deep learning succeeded in part because of a set of inductive biases. However, it required additional ones in order to go from good in-distribution generalisation in highly supervised learning tasks to strong out-of-distribution generalisation and transfer learning to new tasks with low sample complexity.

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