Search Results for author:
Found 29 papers, 12 papers with code
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
These sequence-level latent parameters for each sequence are modeled as latent Dirichlet random variables and parameterized by a set of deterministic database-level hyper-parameters.
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
This paper discusses how to efficiently choose from $n$ unknowndistributions the $k$ ones whose means are the greatest by a certainmetric, up to a small relative error.
BRITS: Bidirectional Recurrent Imputation for Time Series
It is ubiquitous that time series contains many missing values.
General Classification
Multivariate Time Series Forecasting
+5
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
LMVP: Video Predictor with Leaked Motion Information
We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs.
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions.
Relational State-Space Model for Stochastic Multi-Object Systems
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.
Nonparametric Estimation of the Fisher Information and Its Applications
First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived.
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
Forecast with Forecasts: Diversity Matters
In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features.
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.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
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.
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Class-imbalance is a common problem in machine learning practice.
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.
Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability
A large number of novel two-dimensional (2D) materials are constantly discovered and deposed into the databases.
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.
OpenFE: Automated Feature Generation with Expert-level Performance
In the two competitions, features generated by OpenFE with a simple baseline model can beat 99. 3% and 99. 6% data science teams respectively.
Autonomous Driving Simulator based on Neurorobotics Platform
There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive.
UADB: Unsupervised Anomaly Detection Booster
This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.
Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series
Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series discrepancy.
What Matters to Enhance Traffic Rule Compliance of Imitation Learning for Automated Driving
More research attention has recently been given to end-to-end autonomous driving technologies where the entire driving pipeline is replaced with a single neural network because of its simpler structure and faster inference time.
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining
In this work, we make key technical contributions that are tailored to the numerical properties of time-series data and allow the model to scale to large datasets, e. g., millions of temporal sequences.
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and Tracking
To address these challenges, we introduce a diffusion model that explicitly learns the shape and motion distribution of non-rigid objects through an iterative denoising process of compressed latent representations.
Deep Learning for Multivariate Time Series Imputation: A Survey
In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods.
Qubit-Wise Architecture Search Method for Variational Quantum Circuits
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates.
