24 citations · 54 across the 11 of their papers we have counts for
5 papers · 1 filter
The neuroconnectionist research programme
Adrien Doerig, Rowan Sommers, Katja Seeliger +8
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been la…
Learning domain-specific causal discovery from time series
Xinyue Wang, Konrad Paul Kording
Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning. Techniques for CD encompass randomized experiments, which are generally u…
Neural Networks as Paths through the Space of Representations
Richard D. Lange, Devin Kwok, Jordan Matelsky +3
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to…
Clustering units in neural networks: upstream vs downstream information
Richard D. Lange, David S. Rolnick, Konrad P. Kording
It has been hypothesized that some form of "modular" structure in artificial neural networks should be useful for learning, compositionality, and generalization. However, defining…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…