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04/14/2023
The END: An Equivariant Neural Decoder for Quantum Error Correction
Quantum error correction is a critical component for scaling up quantum ...
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01/17/2023
Robust Scheduling with GFlowNets
Finding the best way to schedule operations in a computation graph is a ...
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09/01/2022
Learning Perturbations for Soft-Output Linear MIMO Demappers
Tree-based demappers for multiple-input multiple-output (MIMO) detection...
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07/18/2022
Bayesian Optimization for Macro Placement
Macro placement is the problem of placing memory blocks on a chip canvas...
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07/13/2022
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have sho...
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07/01/2022
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
We propose a novel machine learning method for sampling from the high-di...
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03/04/2022
Neural Simulated Annealing
Simulated annealing (SA) is a stochastic global optimisation technique a...
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10/06/2021
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
We propose a continuous normalizing flow for sampling from the high-dime...
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06/18/2021
Deterministic Gibbs Sampling via Ordinary Differential Equations
Deterministic dynamics is an essential part of many MCMC algorithms, e.g...
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03/08/2021
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning
In this work we develop a quantum field theory formalism for deep learni...
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02/26/2021
Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels
In this work we propose a batch Bayesian optimization method for combina...
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10/21/2020
Probabilistic Numeric Convolutional Neural Networks
Continuous input signals like images and time series that are irregularl...
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10/21/2020
Quantum Deformed Neural Networks
We develop a new quantum neural network layer designed to run efficientl...
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06/11/2019