Tensor network noise characterization for near-term quantum computers
Characterization of noise in current near-term quantum devices is of paramount importance
to fully use their computational power. However, direct quantum process tomography …
to fully use their computational power. However, direct quantum process tomography …
Image deconvolution and point-spread function reconstruction with STARRED: A wavelet-based two-channel method optimized for light-curve extraction
We present starred, a point-spread function (PSF) reconstruction, two-channel
deconvolution, and light-curve extraction method designed for high-precision photometric …
deconvolution, and light-curve extraction method designed for high-precision photometric …
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
Additive manufacturing methods together with topology optimization have enabled the
creation of multiscale structures with controlled spatially-varying material microstructure …
creation of multiscale structures with controlled spatially-varying material microstructure …
Learning constitutive relations from soil moisture data via physically constrained neural networks
The constitutive relations of the Richardson‐Richards equation encode the macroscopic
properties of soil water retention and conductivity. These soil hydraulic functions are …
properties of soil water retention and conductivity. These soil hydraulic functions are …
Towards efficient and scalable training of differentially private deep learning
Differentially private stochastic gradient descent (DP-SGD) is the standard algorithm for
training machine learning models under differential privacy (DP). The most common DP …
training machine learning models under differential privacy (DP). The most common DP …
CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations
Optimal Power Flow (OPF) refers to a wide range of related optimization problems with the
goal of operating power systems efficiently and securely. In the simplest setting, OPF …
goal of operating power systems efficiently and securely. In the simplest setting, OPF …
[HTML][HTML] Learning-based methods for adaptive informative path planning
Abstract adaptive informative path planning (AIPP) is important to many robotics
applications, enabling mobile robots to efficiently collect useful data about initially unknown …
applications, enabling mobile robots to efficiently collect useful data about initially unknown …
Variational Type Graph Autoencoder for Denoising on Event Recommendation
S Zhang, X Meng, Y Zhang - ACM Transactions on Information Systems, 2024 - dl.acm.org
Recommendations for events play a pivotal role in facilitating the discovery of upcoming
intriguing events within Event-Based Social Networks (EBSNs). Previous research has …
intriguing events within Event-Based Social Networks (EBSNs). Previous research has …
Fully First-Order Methods for Decentralized Bilevel Optimization
This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents
only communicate with their neighbors. We propose Decentralized Stochastic Gradient …
only communicate with their neighbors. We propose Decentralized Stochastic Gradient …
Neural network emulator to constrain the high-z IGM thermal state from Lyman-α forest flux autocorrelation function
We present a neural network emulator to constrain the thermal parameters of the
intergalactic medium (IGM) at 5.4≤ z≤ 6.0 using the Lyman-α (Lyα) forest flux auto …
intergalactic medium (IGM) at 5.4≤ z≤ 6.0 using the Lyman-α (Lyα) forest flux auto …