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09/02/2023
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Large language models (LLMs) have recently demonstrated remarkable capab...
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08/08/2023
ALens: An Adaptive Domain-Oriented Abstract Writing Training Tool for Novice Researchers
The significance of novice researchers acquiring proficiency in writing ...
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07/22/2023
Collaboratively Learning Linear Models with Structured Missing Data
We study the problem of collaboratively learning least squares estimates...
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03/27/2023
Parameter estimation for many-particle models from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler
In this work we study systems consisting of a group of moving particles....
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03/06/2023
Machine learning for phase ordering dynamics of charge density waves
We present a machine learning (ML) framework for large-scale dynamical s...
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10/18/2022
ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics
Predicting the behaviors of pedestrian crowds is of critical importance ...
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10/16/2022
Dimension free ridge regression
Random matrix theory has become a widely useful tool in high-dimensional...
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08/29/2022
Real-Time Mask Detection Based on SSD-MobileNetV2
After the outbreak of COVID-19, mask detection, as the most convenient a...
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06/24/2022
How many labelers do you have? A closer look at gold-standard labels
The construction of most supervised learning datasets revolves around co...
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02/20/2022
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression
We examine the necessity of interpolation in overparameterized models, t...
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12/14/2021
The high-dimensional asymptotics of first order methods with random data
We study a class of deterministic flows in ℝ^d× k, parametrized by a ran...
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11/17/2021
Deep learning based on mixed-variable physics informed neural network for solving fluid dynamics without simulation data
Deep learning method has attracted tremendous attention to handle fluid ...
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07/13/2020
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
Natural policy gradient (NPG) methods are among the most widely used pol...
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01/14/2020
Inference for linear forms of eigenvectors under minimal eigenvalue separation: Asymmetry and heteroscedasticity
A fundamental task that spans numerous applications is inference and unc...
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11/30/2018
