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Found 11 papers, 5 papers with code
Learning Differential Operators for Interpretable Time Series Modeling
In this work, we propose an learning framework that can automatically obtain interpretable PDE models from sequential data.
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures
It uses less than 5% FLOPS compared with previous SOTA methods on the largest benchmark dataset.
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.
Dynamic Graph Representation Learning with Fourier Temporal State Embedding
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks
In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.
SEEK: Segmented Embedding of Knowledge Graphs
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.
Ranked #1 on
Link Prediction
on YAGO37
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments within the sentence scope.
Ranked #4 on
Document-level Event Extraction
on ChFinAnn
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction
To demonstrate the effectiveness of DIAG-NRE, we apply it to two real-world datasets and present both significant and interpretable improvements over state-of-the-art methods.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines.
