Learning in the brain is thought to emerge from local synaptic and circuit-level processes rather than from a globally designed blueprint. We formulate Self-Assembling Learning as the problem of how ...

As modern computing becomes limited by energy consumption, there is growing interest in physical computing paradigms that can operate closer to fundamental thermodynamic limits. Thermodynamic ...

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Abstract: This paper presents a discrete-time Anti-Disturbance Zeroing Neural Network (AD-ZNN) model for robust trajectory tracking of robotic manipulators under time-varying disturbances. By ...

Machine learning is a branch of AI focused on building computer systems that learn from data. The breadth of ML techniques enables software applications to improve their performance over time. ML ...

Neural network training can be slow and energy-expensive due to the frequent transfer of weight data between digital memory and processing units. Neuromorphic systems can accelerate neural networks by ...

XGBoost is defined as a scalable and efficient implementation of Gradient Boosting, popularly leveraged for supervised machine learning tasks. Random Forest is defined as an ensemble learning method ...