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Found 18 papers, 7 papers with code
A Comprehensive Survey on AI-based Methods for Patents
This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction.
Contextualized Structural Self-supervised Learning for Ontology Matching
Ontology matching (OM) entails the identification of semantic relationships between concepts within two or more knowledge graphs (KGs) and serves as a critical step in integrating KGs from various sources.
Accelerated Neural Network Training with Rooted Logistic Objectives
Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss.
SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features
In comparison to conventional RGB cameras, the superior temporal resolution of event cameras allows them to capture rich information between frames, making them prime candidates for object tracking.
Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis
Often, deep network models are purely inductive during training and while performing inference on unseen data.
Ranked #7 on
Visual Question Answering
on VQA v2 test-dev
Zero-Shot Multi-Modal Artist-Controlled Retrieval and Exploration of 3D Object Sets
When creating 3D content, highly specialized skills are generally needed to design and generate models of objects and other assets by hand.
Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30
Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data.
Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation
As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data.
VRConvMF: Visual Recurrent Convolutional Matrix Factorization for Movie Recommendation
Fortunately, context-aware recommender systems can alleviate the sparsity problem by making use of some auxiliary information, such as the information of both the users and items.
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation
Our method contains two major components: one is the pseudo-reversible neural network (PRNN) module that effectively transforms high-dimensional input variables to low-dimensional active variables, and the other is the synthesized regression module for approximating function values based on the transformed data in the low-dimensional space.
A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling
The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems.
Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver
Partial differential equations are often used to model various physical phenomena, such as heat diffusion, wave propagation, fluid dynamics, elasticity, electrodynamics and image processing, and many analytic approaches or traditional numerical methods have been developed and widely used for their solutions.
Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations
A dimension reduction method based on the "Nonlinear Level set Learning" (NLL) approach is presented for the pointwise prediction of functions which have been sparsely sampled.
Unified Robust Boosting
However, there is a lack of weighted estimation to indicate the outlier status of the observations.
Computation 62H30, 62G35, 68Q32
A ROM-accelerated parallel-in-time preconditioner for solving all-at-once systems from evolutionary PDEs
In this paper we propose to use model reduction techniques for speeding up the diagonalization-based parallel-in-time (ParaDIAG) preconditioner, for iteratively solving all-at-once systems from evolutionary PDEs.
Numerical Analysis Numerical Analysis Dynamical Systems
Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey
In this paper, we survey the ROMs developed for the QGE in order to understand their potential in efficient numerical simulations of more complex ocean flows: We explain how classical numerical methods for the QGE are used to generate the ROM basis functions, we outline the main steps in the construction of projection-based ROMs (with a particular focus on the under-resolved regime, when the closure problem needs to be addressed), we illustrate the ROMs in the numerical simulation of the QGE for various settings, and we present several potential future research avenues in the ROM exploration of the QGE and more complex models of geophysical flows.
Fluid Dynamics Numerical Analysis Numerical Analysis
MM for Penalized Estimation
The general framework is to minimize a loss function subject to a penalty designed to generate sparse variable selection.
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
