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02/08/2022
Systematically improving existing k-means initialization algorithms at nearly no cost, by pairwise-nearest-neighbor smoothing
We present a meta-method for initializing (seeding) the k-means clusteri...
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02/07/2022
Deep Networks on Toroids: Removing Symmetries Reveals the Structure of Flat Regions in the Landscape Geometry
We systematize the approach to the investigation of deep neural network ...
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12/19/2021
Quantum Approximate Optimization Algorithm applied to the binary perceptron
We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimi...
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10/27/2020
Wide flat minima and optimal generalization in classifying high-dimensional Gaussian mixtures
We analyze the connection between minimizers with good generalizing prop...
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08/01/2020
Ergodic Annealing
Simulated Annealing is the crowning glory of Markov Chain Monte Carlo Me...
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06/14/2020
Entropic gradient descent algorithms and wide flat minima
The properties of flat minima in the empirical risk landscape of neural ...
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05/03/2020
Multialternative Neural Decision Processes
We introduce an algorithmic decision process for multialternative choice...
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11/15/2019
Clustering of solutions in the symmetric binary perceptron
The geometrical features of the (non-convex) loss landscape of neural ne...
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09/29/2019
Natural representation of composite data with replicated autoencoders
Generative processes in biology and other fields often produce data that...
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07/17/2019
On the geometry of solutions and on the capacity of multi-layer neural networks with ReLU activations
Rectified Linear Units (ReLU) have become the main model for the neural ...
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05/20/2019
Shaping the learning landscape in neural networks around wide flat minima
Learning in Deep Neural Networks (DNN) takes place by minimizing a non-c...
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05/01/2019
Recombinator-k-means: Enhancing k-means++ by seeding from pools of previous runs
We present a heuristic algorithm, called recombinator-k-means, that can ...
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10/26/2017
On the role of synaptic stochasticity in training low-precision neural networks
Stochasticity and limited precision of synaptic weights in neural networ...
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07/03/2017
Parle: parallelizing stochastic gradient descent
We propose a new algorithm called Parle for parallel training of deep ne...
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06/26/2017
Efficiency of quantum versus classical annealing in non-convex learning problems
Quantum annealers aim at solving non-convex optimization problems by exp...
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11/06/2016
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
This paper proposes a new optimization algorithm called Entropy-SGD for ...
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05/20/2016
Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes
In artificial neural networks, learning from data is a computationally d...
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02/12/2016
Learning may need only a few bits of synaptic precision
Learning in neural networks poses peculiar challenges when using discret...
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11/18/2015
Local entropy as a measure for sampling solutions in Constraint Satisfaction Problems
We introduce a novel Entropy-driven Monte Carlo (EdMC) strategy to effic...
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09/18/2015
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
We show that discrete synaptic weights can be efficiently used for learn...
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05/20/2015
