Given the rapidly growing scale and resource requirements of machine learning
applications, the idea of building more efficient learning machines much closer to the laws of …

Biologically plausible neural networks have demonstrated efficiency in learning and
recognizing patterns in data. This paper proposes a general online unsupervised algorithm …

In the quest for energy-efficient artificial intelligence systems, resistor networks are attracting
interest as an alternative to conventional GPU-based neural networks. These networks …

Physical neural networks (PNNs) are a class of neural-like networks that leverage the
properties of physical systems to perform computation. While PNNs are so far a niche …

Physical learning is an emerging paradigm in science and engineering whereby (meta)
materials acquire desired macroscopic behaviors by exposure to examples. So far, it has …

Learning to change shape is a fundamental strategy of adaptation and evolution of living
organisms, from bacteria and cells to tissues and animals. Human-made materials can also …

The widespread adoption of machine learning and artificial intelligence in all branches of
science and technology has created a need for energy-efficient, alternative hardware …