Search Results for author:
Found 21 papers, 7 papers with code
Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored.
Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems.
Exploring the Robustness of In-Context Learning with Noisy Labels
In this paper, inspired by prior research that studies ICL ability using simple function classes, we take a closer look at this problem by investigating the robustness of Transformers against noisy labels.
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
CUCL: Codebook for Unsupervised Continual Learning
To address this issue, after analyzing the phenomenon and identifying the lack of diversity as a vital factor, we propose a method named Codebook for Unsupervised Continual Learning (CUCL) which promotes the model to learn discriminative features to complete the class boundary.
From Authority-Respect to Grassroots-Dissent: Degree-Weighted Social Learning and Convergence Speed
Opinions are influenced by neighbors, with varying degrees of emphasis based on their connections.
ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models
Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
FaceChain: A Playground for Human-centric Artificial Intelligence Generated Content
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
MCDIP-ADMM: Overcoming Overfitting in DIP-based CT reconstruction
To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the $l_1$ prior, the total variation prior, and the deep image prior (DIP).
Machine learning for phase ordering dynamics of charge density waves
We present a machine learning (ML) framework for large-scale dynamical simulations of charge density wave (CDW) states.
ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics
Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems.
Dimension free ridge regression
However, random matrix theory is largely focused on the proportional asymptotics in which the number of columns grows proportionally to the number of rows of the data matrix.
Real-Time Mask Detection Based on SSD-MobileNetV2
An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff.
How many labelers do you have? A closer look at gold-standard labels
The construction of most supervised learning datasets revolves around collecting multiple labels for each instance, then aggregating the labels to form a type of "gold-standard".
IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing client data.
Memorize to Generalize: on the Necessity of Interpolation in High Dimensional Linear Regression
We examine the necessity of interpolation in overparameterized models, that is, when achieving optimal predictive risk in machine learning problems requires (nearly) interpolating the training data.
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization
This class of methods is often applied in conjunction with entropy regularization -- an algorithmic scheme that encourages exploration -- and is closely related to soft policy iteration and trust region policy optimization.
Tackling small eigen-gaps: Fine-grained eigenvector estimation and inference under heteroscedastic noise
This paper aims to address two fundamental challenges arising in eigenvector estimation and inference for a low-rank matrix from noisy observations: (1) how to estimate an unknown eigenvector when the eigen-gap (i. e. the spacing between the associated eigenvalue and the rest of the spectrum) is particularly small; (2) how to perform estimation and inference on linear functionals of an eigenvector -- a sort of "fine-grained" statistical reasoning that goes far beyond the usual $\ell_2$ analysis.
Geometry, Computation, and Optimality in Stochastic Optimization
We study computational and statistical consequences of problem geometry in stochastic and online optimization.
Asymmetry Helps: Eigenvalue and Eigenvector Analyses of Asymmetrically Perturbed Low-Rank Matrices
The aim is to estimate the leading eigenvalue and eigenvector of $\mathbf{M}^{\star}$.
Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation
Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web.
