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Found 16 papers, 7 papers with code
PAC-FNO: Parallel-Structured All-Component Fourier Neural Operators for Recognizing Low-Quality Images
Extensively evaluating methods with seven image recognition benchmarks, we show that the proposed PAC-FNO improves the performance of existing baseline models on images with various resolutions by up to 77. 1% and various types of natural variations in the images at inference.
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks and opened a new field.
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation.
SPI-GAN: Denoising Diffusion GANs with Straight-Path Interpolations
Score-based generative models (SGMs) show the state-of-the-art sampling quality and diversity.
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough Differential Equations
The problem of processing very long time-series data (e. g., a length of more than 10, 000) is a long-standing research problem in machine learning.
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting
Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks.
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis
First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.
Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis
First, we can further improve the synthesis quality, by decreasing the negative log-density of real records in the process of adversarial training.
Linear, or Non-Linear, That is the Question!
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.
IIT-GAN: Irregular and Intermittent Time-series Synthesis with Generative Adversarial Networks
In this paper, we solve the problem of synthesizing irregular and intermittent time-series where values can be missing and may not have specific frequencies, which is far more challenging than existing settings.
LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
Among various such mobile billboards, taxicab rooftop devices are emerging in the market as a brand new media.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF).
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Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) presented a new paradigm to construct (continuous-time) neural networks.
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Synthesizing tabular data is attracting much attention these days for various purposes.
