Search Results for author:
Found 13 papers, 1 papers with code
On How Iterative Magnitude Pruning Discovers Local Receptive Fields in Fully Connected Neural Networks
We develop a new method for measuring the effect of individual weights on the statistics of the FCN representations ("cavity method"), which allows us to find evidence in support of this hypothesis.
Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers
Deep linear networks have been extensively studied, as they provide simplified models of deep learning.
Machine learning at the mesoscale: a computation-dissipation bottleneck
The cost of information processing in physical systems calls for a trade-off between performance and energetic expenditure.
Neural networks trained with SGD learn distributions of increasing complexity
The ability of deep neural networks to generalise well even when they interpolate their training data has been explained using various "simplicity biases".
Data-driven emergence of convolutional structure in neural networks
Here, we show how initially fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs, resulting in localised, space-tiling receptive fields.
Input correlations impede suppression of chaos and learning in balanced rate networks
To study this phenomenon we develop a non-stationary dynamic mean-field theory that determines how the activity statistics and largest Lyapunov exponent depend on frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input.
Epidemic mitigation by statistical inference from contact tracing data
We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.
Optimal Learning with Excitatory and Inhibitory synapses
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits.
Training dynamically balanced excitatory-inhibitory networks
The construction of biologically plausible models of neural circuits is crucial for understanding the computational properties of the nervous system.
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
We define a novel measure, which we call the "robust ensemble" (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions.
Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model
Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles.
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to efficiently sample solutions of random Constraint Satisfaction Problems (CSPs).
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
We also show that the dense regions are surprisingly accessible by simple learning protocols, and that these synaptic configurations are robust to perturbations and generalize better than typical solutions.
