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Found 20 papers, 2 papers with code
Quantum Compiling with Reinforcement Learning on a Superconducting Processor
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology.
Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent
This paper studies the robust Hankel recovery problem, which simultaneously removes the sparse outliers and fulfills missing entries from the partial observation.
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction
Experiments across various prompts, PLMs, and benchmarks show that our approach can not only correct the overfitted performance caused by prompt bias, but also significantly improve the prompt retrieval capability (up to 10% absolute performance gain).
Neural Network Approximation for Pessimistic Offline Reinforcement Learning
In this paper, we establish a non-asymptotic estimation error of pessimistic offline RL using general neural network approximation with $\mathcal{C}$-mixing data regarding the structure of networks, the dimension of datasets, and the concentrability of data coverage, under mild assumptions.
Provable Advantage of Parameterized Quantum Circuit in Function Approximation
To achieve this, we utilize techniques from quantum signal processing and linear combinations of unitaries to construct PQCs that implement multivariate polynomials.
Current density impedance imaging with PINNs
In this paper, we introduce CDII-PINNs, a computationally efficient method for solving CDII using PINNs in the framework of Tikhonov regularization.
GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs
With the recent study of deep learning in scientific computation, the Physics-Informed Neural Networks (PINNs) method has drawn widespread attention for solving Partial Differential Equations (PDEs).
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets.
Imaging Conductivity from Current Density Magnitude using Neural Networks
Conductivity imaging represents one of the most important tasks in medical imaging.
A Data-Driven Line Search Rule for Support Recovery in High-dimensional Data Analysis
In this work, we consider the algorithm to the (nonlinear) regression problems with $\ell_0$ penalty.
Coordinate Descent for MCP/SCAD Penalized Least Squares Converges Linearly
Recovering sparse signals from observed data is an important topic in signal/imaging processing, statistics and machine learning.
Deep Neural Networks with ReLU-Sine-Exponential Activations Break Curse of Dimensionality in Approximation on Hölder Class
In this paper, we construct neural networks with ReLU, sine and $2^x$ as activation functions.
Generative Learning With Euler Particle Transport
The key task in training is the estimation of the density ratios or differences that determine the residual maps.
On Newton Screening
Based on this KKT system, a built-in working set with a relatively small size is first determined using the sum of primal and dual variables generated from the previous iteration, then the primal variable is updated by solving a least-squares problem on the working set and the dual variable updated based on a closed-form expression.
A Support Detection and Root Finding Approach for Learning High-dimensional Generalized Linear Models
Feature selection is important for modeling high-dimensional data, where the number of variables can be much larger than the sample size.
A stochastic alternating minimizing method for sparse phase retrieval
Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention.
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for the LASSO and Enet in sparse, high-dimensional linear regression.
Oblique Stripe Removal in Remote Sensing Images via Oriented Variation
Destriping is a classical problem in remote sensing image processing.
A Primal Dual Active Set with Continuation Algorithm for the \ell^0-Regularized Optimization Problem
We develop a primal dual active set with continuation algorithm for solving the \ell^0-regularized least-squares problem that frequently arises in compressed sensing.
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.
