4 papers
Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks
Gianluca Bencomo, Max Gupta, Ioana Marinescu +2
Artificial neural networks can acquire many aspects of human knowledge from data, making them promising as models of human learning. But what those networks can learn depends upon…
Learning Human-Aligned Representations with Contrastive Learning and Generative Similarity
Raja Marjieh, Sreejan Kumar, Declan Campbell +4
Humans rely on effective representations to learn from few examples and abstract useful information from sensory data. Inducing such representations in machine learning models has…
A Metalearned Neural Circuit for Nonparametric Bayesian Inference
Jake C. Snell, Gianluca Bencomo, Thomas L. Griffiths
Most applications of machine learning to classification assume a closed set of balanced classes. This is at odds with the real world, where class occurrence statistics often follow…
Implicit Maximum a Posteriori Filtering via Adaptive Optimization
Gianluca M. Bencomo, Jake C. Snell, Thomas L. Griffiths
Bayesian filtering approximates the true underlying behavior of a time-varying system by inverting an explicit generative model to convert noisy measurements into state estimates.…