research
∙
08/29/2023
MadSGM: Multivariate Anomaly Detection with Score-based Generative Models
The time-series anomaly detection is one of the most fundamental tasks f...
share
research
∙
06/27/2023
Precursor-of-Anomaly Detection for Irregular Time Series
Anomaly detection is an important field that aims to identify unexpected...
research
∙
05/09/2023
Hawkes Process based on Controlled Differential Equations
Hawkes processes are a popular framework to model the occurrence of sequ...
research
∙
04/25/2023
CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis
With growing attention to tabular data these days, the attempt to apply ...
research
∙
03/20/2023
Graph Neural Rough Differential Equations for Traffic Forecasting
Traffic forecasting is one of the most popular spatio-temporal tasks in ...
research
∙
01/23/2023
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Co-exploration of an optimal neural architecture and its hardware accele...
research
∙
01/20/2023
Regular Time-series Generation using SGM
Score-based generative models (SGMs) are generative models that are in t...
research
∙
01/11/2023
Learnable Path in Neural Controlled Differential Equations
Neural controlled differential equations (NCDEs), which are continuous a...
research
∙
11/25/2022
GREAD: Graph Neural Reaction-Diffusion Equations
Graph neural networks (GNNs) are one of the most popular research topics...
research
∙
11/22/2022
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Forecasting future outcomes from recent time series data is not easy, es...
research
∙
11/17/2022
Perturbation-Recovery Method for Recommendation
Collaborative filtering is one of the most influential recommender syste...
research
∙
11/08/2022
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Recommender systems are a long-standing research problem in data mining ...
research
∙
10/10/2022
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting
Continuous-time dynamics models, such as neural ordinary differential eq...
research
∙
10/08/2022
STaSy: Score-based Tabular data Synthesis
Tabular data synthesis is a long-standing research topic in machine lear...
research
∙
10/05/2022
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks
Time series synthesis is an important research topic in the field of dee...
research
∙
09/03/2022
Phishing URL Detection: A Network-based Approach Robust to Evasion
Many cyberattacks start with disseminating phishing URLs. When clicking ...
research
∙
08/30/2022
Prediction-based One-shot Dynamic Parking Pricing
Many U.S. metropolitan cities are notorious for their severe shortage of...
research
∙
08/17/2022
An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models
Tabular data typically contains private and important information; thus,...
research
∙
07/13/2022
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation
Recent work by Xia et al. leveraged the continuous-limit of the classica...
research
∙
06/29/2022
SPI-GAN: Distilling Score-based Generative Models with Straight-Path Interpolations
Score-based generative models (SGMs) are a recently proposed paradigm fo...
research
∙
06/17/2022
SOS: Score-based Oversampling for Tabular Data
Score-based generative models (SGMs) are a recent breakthrough in genera...
research
∙
04/19/2022
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 ...
research
∙
04/19/2022
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 tr...
research
∙
03/31/2022
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Model quantization is considered as a promising method to greatly reduce...
research
∙
02/08/2022
Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis
Tabular data synthesis has received wide attention in the literature. Th...
research
∙
12/07/2021
Graph Neural Controlled Differential Equations for Traffic Forecasting
Traffic forecasting is one of the most popular spatio-temporal tasks in ...
research
∙
11/14/2021
Linear, or Non-Linear, That is the Question!
There were fierce debates on whether the non-linear embedding propagatio...
research
∙
11/11/2021
Climate Modeling with Neural Diffusion Equations
Owing to the remarkable development of deep learning technology, there h...
research
∙
11/04/2021
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
Model quantization is known as a promising method to compress deep neura...
research
∙
09/04/2021
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting
Neural networks inspired by differential equations have proliferated for...
research
∙
08/11/2021
LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising
Mobile digital billboards are an effective way to augment brand-awarenes...
research
∙
08/08/2021
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
Collaborative filtering (CF) is a long-standing problem of recommender s...
research
∙
05/31/2021
Large-Scale Data-Driven Airline Market Influence Maximization
We present a prediction-driven optimization framework to maximize the ma...
research
∙
05/31/2021
OCT-GAN: Neural ODE-based Conditional Tabular GANs
Synthesizing tabular data is attracting much attention these days for va...
research
∙
05/31/2021
ACE-NODE: Attentive Co-Evolving Neural Ordinary Differential Equations
Neural ordinary differential equations (NODEs) presented a new paradigm ...
research
∙
12/04/2020
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
We present a method for learning dynamics of complex physical processes ...
research
∙
11/20/2020
Detecting Universal Trigger's Adversarial Attack with Honeypot
The Universal Trigger (UniTrigger) is a recently-proposed powerful adver...
research
∙
11/17/2020
SIENA: Stochastic Multi-Expert Neural Patcher
Neural network (NN) models that are solely trained to maximize the likel...
research
∙
11/10/2020
Two-stage Training of Graph Neural Networks for Graph Classification
Graph Neural Networks (GNNs) have received massive attention in the fiel...
research
∙
06/28/2020
Human Mobility during COVID-19 in the Context of Mild Social Distancing: Implications for Technological Interventions
The COVID-19 pandemic has brought both tangible and intangible damage to...
research
∙
01/24/2020
MONSTOR: An Inductive Approach for Estimating and Maximizing Influence over Unseen Social Networks
Influence maximization (IM) is one of the most important problems in soc...
research
∙
06/11/2019
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business,...
research
∙
10/03/2018
GI-OHMS: Graphical Inference to Detect Overlapping Communities
Discovery of communities in complex networks is a topic of considerable ...
research
∙
06/09/2018
Data Synthesis based on Generative Adversarial Networks
Privacy is an important concern for our society where sharing data with ...
research
∙
05/07/2018
MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation
Recently, generative adversarial networks (GANs) have shown promising pe...
research
∙
12/05/2016