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Taming chaos in neural networks: A biologically plausible way

A new framework that causes artificial neural networks to mimic how real neural networks operate in the brain has been ...
San Francisco Fed

Estimating Nonlinear Heterogeneous Agent Models with Neural Networks

We leverage recent advancements in machine learning to develop an integrated method to solve globally and estimate models featuring agent heterogeneity, nonlinear constraints, and aggregate ...

Humans and artificial neural networks exhibit some similar patterns during learning

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge ...
San Francisco Fed

Deep Neural Network Estimation in Panel Data Models

In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, ...

Transformer AI models outperform neural networks in stock market prediction, study shows

Like other sectors of society, artificial intelligence is fundamentally changing how investors, traders and companies make decisions in financial markets. AI models have the ability to analyze massive ...

A unified model of memory and perception: How Hebbian learning explains our recall of past events

A collaboration between SISSA's Physics and Neuroscience groups has taken a step forward in understanding how memories are ...