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Found 38 papers, 12 papers with code
CSST Strong Lensing Preparation: a Framework for Detecting Strong Lenses in the Multi-color Imaging Survey by the China Survey Space Telescope (CSST)
To overcome these challenges, we have developed a framework based on a hierarchical visual Transformer with a sliding window technique to search for strong lensing systems within entire images.
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems.
Anti-symmetric Barron functions and their approximation with sums of determinants
A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles.
Convergence of variational Monte Carlo simulation and scale-invariant pre-training
We provide theoretical convergence bounds for the variational Monte Carlo (VMC) method as applied to optimize neural network wave functions for the electronic structure problem.
Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning
Specifically, relying on the structural models of revenue and price, we establish the identifiability condition of an optimal pricing strategy under endogeneity with the help of invalid instrumental variables.
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework controlling FDR (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple-hypothesis testing.
Efficient anti-symmetrization of a neural network layer by taming the sign problem
We show that the anti-symmetric projection of a two-layer neural network can be evaluated efficiently, opening the door to using a generic antisymmetric layer as a building block in anti-symmetric neural network Ansatzes.
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices.
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model
Depression is a global mental health problem, the worst case of which can lead to suicide.
Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation
We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks.
Adaptive Weighted Multi-View Clustering
Learning multi-view data is an emerging problem in machine learning research, and nonnegative matrix factorization (NMF) is a popular dimensionality-reduction method for integrating information from multiple views.
Mixture of Linear Models Co-supervised by Deep Neural Networks
Our main idea is a mixture of discriminative models that is trained with the guidance from a DNN.
Filters for ISI Suppression in Molecular Communication via Diffusion
The extent that ISI and noise are suppressed in an MCvD system determines its effectiveness, especially at a high data rate.
NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science.
Time-dependent unbounded Hamiltonian simulation with vector norm scaling
We demonstrate that under suitable assumptions of the Hamiltonian and the initial vector, if the error is measured in terms of the vector norm, the computational cost may not increase at all as the norm of the Hamiltonian increases using Trotter type methods.
Quantum Physics Numerical Analysis Numerical Analysis
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
Variational quantum eigensolvers have recently received increased attention, as they enable the use of quantum computing devices to find solutions to complex problems, such as the ground energy and ground state of strongly-correlated quantum many-body systems.
Persistent Emission Properties of SGR J1935+2154 During Its 2020 Active Episode
Our investigations of the XMM-Newton and Chandra spectra with a variety of phenomenological and physically-motivated models, concluded that the magnetic field topology of SGR J1935+2154 is most likely highly non-dipolar.
High Energy Astrophysical Phenomena
Efficient Long-Range Convolutions for Point Clouds
We showcase this framework by introducing a neural network architecture that combines LRC-layers with short-range convolutional layers to accurately learn the energy and force associated with a $N$-body potential.
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
We propose a generalized QAOA called CD-QAOA, which is inspired by the counterdiabatic driving procedure, designed for quantum many-body systems and optimized using a reinforcement learning (RL) approach.
Random circuit block-encoded matrix and a proposal of quantum LINPACK benchmark
The success of the quantum LINPACK benchmark should be viewed as the minimal requirement for a quantum computer to perform a useful task of solving linear algebra problems, such as linear systems of equations.
Quantum Physics Numerical Analysis Numerical Analysis
Interpretable Deep Representation Learning from Temporal Multi-view Data
In many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse measurements across time that exhibit time-dependent heterogeneous properties.
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles.
Computational Physics
Efficient phase-factor evaluation in quantum signal processing
Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers.
Quantum Physics Optimization and Control Computational Physics
Learning the mapping $\mathbf{x}\mapsto \sum_{i=1}^d x_i^2$: the cost of finding the needle in a haystack
When such structural information is not available, and we may only use a dense neural network, the optimization procedure to find the sparse network embedded in the dense network is similar to finding the needle in a haystack, using a given number of samples of the function.
Policy Gradient based Quantum Approximate Optimization Algorithm
Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control.
Distributed Pinning Control Design for Probabilistic Boolean Networks
Owing to the stochasticity, the uniform state feedback controllers, which is independent of switching signal, might be out of work.
Deep Variable-Block Chain with Adaptive Variable Selection
In this paper, we propose a framework that imposes on blocks of variables a chain structure obtained by step-wise greedy search so that the DNN architecture can leverage the constructed grid.
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the electron density as the linear combination of contributions from many local clusters.
Explaining Deep Learning Models -- A Bayesian Non-parametric Approach
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.
Optimization and Control
A multiscale neural network based on hierarchical nested bases
In recent years, deep learning has led to impressive results in many fields.
Numerical Analysis
A multiscale neural network based on hierarchical matrices
This network generalizes the latter to the nonlinear case by introducing a local deep neural network at each spatial scale.
Numerical Analysis
Variational formulation for Wannier functions with entangled band structure
When paired with an initial guess based on the selected columns of the density matrix (SCDM) method, our method can robustly find Wannier functions for systems with entangled band structure.
Computational Physics Numerical Analysis Chemical Physics 65Z05, 82D25, 65F30, 65K10
Towards Interrogating Discriminative Machine Learning Models
Our results indicate that the proposed approach not only outperforms the state-of-the-art technique in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of a learning model.
A General Model for Robust Tensor Factorization with Unknown Noise
We provide two versions of the algorithm with different tensor factorization operations, i. e., CP factorization and Tucker factorization.
Disentanglement via entanglement: A unified method for Wannier localization
Currently, the most widely used method for treating systems with entangled eigenvalues is to first obtain a reduced subspace (often referred to as disentanglement) and then to solve the Wannier localization problem by treating the reduced subspace as an isolated system.
Computational Physics Chemical Physics 65Z05
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.
