70 citations · 348 across the 10 of their papers we have counts for
10 papers
QUOTAS: A new research platform for the data-driven investigation of black holes
Priyamvada Natarajan, Kwok Sun Tang, Robert McGibbon +7
We present QUOTAS, a novel research platform for the data-driven investigation of super-massive black hole (SMBH) populations. While SMBH data sets -- observations and simulations…
Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
Guillaume Verdon, Jacob Marks, Sasha Nanda +2
We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for qu…
Quantum Graph Neural Networks
Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica +3
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and…
TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
Martin Ganahl, Ashley Milsted, Stefan Leichenauer +2
We use TensorNetwork [C. Roberts et al., arXiv: 1905.01330], a recently developed API for performing tensor network contractions using accelerated backends such as TensorFlow, to i…
TensorNetwork for Machine Learning
Stavros Efthymiou, Jack Hidary, Stefan Leichenauer
We demonstrate the use of tensor networks for image classification with the TensorNetwork open source library. We explain in detail the encoding of image data into a matrix product…
TensorNetwork on TensorFlow: A Spin Chain Application Using Tree Tensor Networks
Ashley Milsted, Martin Ganahl, Stefan Leichenauer +2
TensorNetwork is an open source library for implementing tensor network algorithms in TensorFlow. We describe a tree tensor network (TTN) algorithm for approximating the ground sta…
TensorNetwork: A Library for Physics and Machine Learning
Chase Roberts, Ashley Milsted, Martin Ganahl +6
TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body…
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Qianli Liao, Brando Miranda, Andrzej Banburski +2
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors? Better understanding of this question of generalizat…
Theory IIIb: Generalization in Deep Networks
Tomaso Poggio, Qianli Liao, Brando Miranda +3
A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when inc…
Theory of Deep Learning III: explaining the non-overfitting puzzle
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao +5
A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on ra…