activity
20162024
most citedFast and Efficient Stochastic Optimization for Analytic Continuation

18 citations · 42 across the 16 of their papers we have counts for

collaborators

6 papers

math.OC20237 cited

A Score-based Nonlinear Filter for Data Assimilation

Feng Bao, Zezhong Zhang, Guannan Zhang

We introduce a score-based generative sampling method for solving the nonlinear filtering problem with robust accuracy. A major drawback of existing nonlinear filtering methods, e.…

math.NA20231 cited

A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial distributions

Minglei Yang, Pengjun Wang, Diego del-Castillo-Negrete +2

We present a pseudo-reversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with different initial distributi…

cs.LG20231 cited

Who Would be Interested in Services? An Entity Graph Learning System for User Targeting

Dan Yang, Binbin Hu, Xiaoyan Yang +4

With the growing popularity of various mobile devices, user targeting has received a growing amount of attention, which aims at effectively and efficiently locating target users th…

cs.LG2023

GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning

Weifan Wang, Binbin Hu, Zhicheng Peng +5

Recently, the growth of service platforms brings great convenience to both users and merchants, where the service search engine plays a vital role in improving the user experience…

cs.LG2021

Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation

Yuankai Teng, Zhu Wang, Lili Ju +2

Due to the curse of dimensionality and the limitation on training data, approximating high-dimensional functions is a very challenging task even for powerful deep neural networks.…

cond-mat.str-el201618 cited

Fast and Efficient Stochastic Optimization for Analytic Continuation

F. Bao, Y. Tang, M. Summers +4

The analytic continuation of imaginary-time quantum Monte Carlo data to extract real-frequency spectra remains a key problem in connecting theory with experiment. Here we present a…