1 code implementation • 2 Feb 2025 • Gil Goldshlager, Jiang Hu, Lin Lin
Due to the ever growing amounts of data leveraged for machine learning and scientific computing, it is increasingly important to develop algorithms that sample only a small portion of the data at a time.
no code implementations • 29 Jan 2025 • Sebastian Pena, Lin Lin, Jia Li
Many existing algorithms cannot recognize new cell types present in only one of the two samples when establishing a correspondence between clusters obtained from two samples.
no code implementations • 21 Oct 2024 • Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang, Lin Lin, Yang Liu, Jiawei Ren
In this paper, we propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance.
no code implementations • 14 Aug 2024 • Senmao Wang, Haifan Gong, Runmeng Cui, Boyao Wan, Yicheng Liu, Zhonglin Hu, Haiqing Yang, Jingyang Zhou, Bo Pan, Lin Lin, Haiyue Jiang
Furthermore, we developed a comprehensive benchmark that contains 165 cases for costal cartilage segmentation.
no code implementations • 24 May 2024 • Jia Li, Lin Lin
Central to our investigation is dimension reduction within the Wasserstein metric space to enhance classification accuracy.
no code implementations • 2 Apr 2024 • Xu Li, Ruiqi Sun, Jiameng Lv, Peng Jia, Nan Li, Chengliang Wei, Zou Hu, Xinzhong Er, Yun Chen, Zhang Ban, Yuedong Fang, Qi Guo, Dezi Liu, Guoliang Li, Lin Lin, Ming Li, Ran Li, Xiaobo Li, Yu Luo, Xianmin Meng, Jundan Nie, Zhaoxiang Qi, Yisheng Qiu, Li Shao, Hao Tian, Lei Wang, Wei Wang, Jingtian Xian, Youhua Xu, Tianmeng Zhang, Xin Zhang, Zhimin Zhou
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.
1 code implementation • 18 Jan 2024 • Gil Goldshlager, Nilin Abrahamsen, Lin Lin
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.
no code implementations • 22 Mar 2023 • Nilin Abrahamsen, Lin Lin
A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles.
no code implementations • 21 Mar 2023 • Nilin Abrahamsen, Zhiyan Ding, Gil Goldshlager, Lin Lin
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.
no code implementations • 24 Feb 2023 • Rui Miao, Zhengling Qi, Cong Shi, Lin Lin
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.
no code implementations • 6 Oct 2022 • Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva, Lin Lin, Vernon M. Chinchilli
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.
no code implementations • 24 May 2022 • Nilin Abrahamsen, Lin Lin
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.
no code implementations • 30 Mar 2022 • Jiahao Yao, Haoya Li, Marin Bukov, Lin Lin, Lexing Ying
Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices.
2 code implementations • 15 Feb 2022 • Ying Shen, Huiyu Yang, Lin Lin
Depression is a global mental health problem, the worst case of which can lead to suicide.
1 code implementation • 7 Dec 2021 • Jeffmin Lin, Gil Goldshlager, Lin Lin
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.
no code implementations • 25 Oct 2021 • Shuo Shuo Liu, Lin Lin
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.
no code implementations • 5 Aug 2021 • Beomseok Seo, Lin Lin, Jia Li
Our main idea is a mixture of discriminative models that is trained with the guidance from a DNN.
no code implementations • 29 Apr 2021 • Ruifeng Zheng, Lin Lin, Hao Yan
The extent that ISI and noise are suppressed in an MCvD system determines its effectiveness, especially at a high data rate.
2 code implementations • 24 Jan 2021 • Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva, Lin Lin, Vernon M. Chinchilli
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science.
no code implementations • 24 Dec 2020 • Dong An, Di Fang, Lin Lin
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
no code implementations • 12 Dec 2020 • Jiahao Yao, Paul Köttering, Hans Gundlach, Lin Lin, Marin Bukov
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.
no code implementations • 3 Dec 2020 • Ersin Gogus, Matthew G. Baring, Chryssa Kouveliotou, Tolga Guver, Lin Lin, Oliver J. Roberts, George Younes, Yuki Kaneko, Alexander J. van der Horst
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
1 code implementation • 11 Oct 2020 • Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez
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.
no code implementations • 7 Oct 2020 • Jiahao Yao, Lin Lin, Marin Bukov
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.
1 code implementation • 7 Jun 2020 • Yulong Dong, Lin Lin
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
no code implementations • 11 May 2020 • Lin Qiu, Vernon M. Chinchilli, Lin Lin
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.
1 code implementation • 1 May 2020 • Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang
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
3 code implementations • 26 Feb 2020 • Yulong Dong, Xiang Meng, K. Birgitta Whaley, Lin Lin
Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers.
Quantum Physics Optimization and Control Computational Physics
no code implementations • 24 Feb 2020 • Jiefu Zhang, Leonardo Zepeda-Núñez, Yuan YAO, Lin Lin
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.
no code implementations • 4 Feb 2020 • Jiahao Yao, Marin Bukov, Lin Lin
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.
no code implementations • 7 Dec 2019 • Lixiang Zhang, Lin Lin, Jia Li
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.
no code implementations • 7 Dec 2019 • Lin Lin, Jinde Cao, Jianquan Lu, Jie Zhong
Owing to the stochasticity, the uniform state feedback controllers, which is independent of switching signal, might be out of work.
no code implementations • 27 Nov 2019 • Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin
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.
no code implementations • NeurIPS 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
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.
no code implementations • 7 Nov 2018 • Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
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.
1 code implementation • 3 Sep 2018 • Jiang Hu, Bo Jiang, Lin Lin, Zaiwen Wen, Yaxiang Yuan
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
1 code implementation • 4 Aug 2018 • Yuwei Fan, Jordi Feliu-Faba, Lin Lin, Lexing Ying, Leonardo Zepeda-Nunez
In recent years, deep learning has led to impressive results in many fields.
Numerical Analysis
1 code implementation • 5 Jul 2018 • Yuwei Fan, Lin Lin, Lexing Ying, Leonardo Zepeda-Nunez
This network generalizes the latter to the nonlinear case by introducing a local deep neural network at each spatial scale.
Numerical Analysis
1 code implementation • 25 Jan 2018 • Anil Damle, Antoine Levitt, Lin Lin
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
no code implementations • 23 May 2017 • Wenbo Guo, Kaixuan Zhang, Lin Lin, Sui Huang, Xinyu Xing
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.
no code implementations • 18 May 2017 • Xi'ai Chen, Zhi Han, Yao Wang, Qian Zhao, Deyu Meng, Lin Lin, Yandong Tang
We provide two versions of the algorithm with different tensor factorization operations, i. e., CP factorization and Tucker factorization.
1 code implementation • 20 Mar 2017 • Anil Damle, Lin Lin
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
no code implementations • 6 Oct 2016 • Qinglong Wang, Wenbo Guo, Alexander G. Ororbia II, Xinyu Xing, Lin Lin, C. Lee Giles, Xue Liu, Peng Liu, Gang Xiong
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.