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Found 5 papers, 1 papers with code
Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics
Emergence, where complex behaviors develop from the interactions of simpler components within a network, plays a crucial role in enhancing neural network capabilities.
Advancing Neural Network Performance through Emergence-Promoting Initialization Scheme
Emergence in machine learning refers to the spontaneous appearance of complex behaviors or capabilities that arise from the scale and structure of training data and model architectures, despite not being explicitly programmed.
Leveraging Quantum Superposition to Infer the Dynamic Behavior of a Spatial-Temporal Neural Network Signaling Model
We show that this class of problems can be formulated and structured to take advantage of quantum superposition and solved efficiently using the Deutsch-Jozsa and Grover quantum algorithms.
Learning without gradient descent encoded by the dynamics of a neurobiological model
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.
Generalizable Machine Learning in Neuroscience using Graph Neural Networks
Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i. e. parameters relevant to lower scales of organization, remains relatively novel.
