Search Results for author:
Found 9 papers, 4 papers with code
AI-accelerated Discovery of Altermagnetic Materials
Altermagnetism, a new magnetic phase, has been theoretically proposed and experimentally verified to be distinct from ferromagnetism and antiferromagnetism.
Variational optimization of the amplitude of neural-network quantum many-body ground states
Here, we split a quantum many-body variational wave function into a multiplication of a real-valued amplitude neural network and a sign structure, and focus on the optimization of the amplitude network while keeping the sign structure fixed.
A simple framework for contrastive learning phases of matter
A main task in condensed-matter physics is to recognize, classify, and characterize phases of matter and the corresponding phase transitions, for which machine learning provides a new class of research tools due to the remarkable development in computing power and algorithms.
Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models
Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models.
Image Dataset Compression Based on Matrix Product States
Based on the MPS structure, we propose a new dataset compression method that compresses datasets by filtering long-range correlation information in task-agnostic scenarios and uses dataset distillation to supplement the information in task-specific scenarios.
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators
This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics.
Compressing LSTM Networks by Matrix Product Operators
In this paper, we propose a matrix product operator(MPO) based neural network architecture to replace the LSTM model.
Networking and Internet Architecture Computational Physics Quantum Physics
A Model Compression Method with Matrix Product Operators for Speech Enhancement
In this paper, we propose a model compression method based on matrix product operators (MPO) to substantially reduce the number of parameters in DNN models for speech enhancement.
Compressing deep neural networks by matrix product operators
A deep neural network is a parametrization of a multilayer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations.
