b'Yuyang Wang'

research

∙ 08/10/2023

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

We introduce AutoGluon-TimeSeries - an open-source AutoML library for pr...

0 Oleksandr Shchur, et al. ∙

research

∙ 07/21/2023

Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting

Diffusion models have achieved state-of-the-art performance in generativ...

0 Marcel Kollovieh, et al. ∙

research

∙ 07/19/2023

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Earth system forecasting has traditionally relied on complex physical mo...

0 Zhihan Gao, et al. ∙

research

∙ 07/12/2023

Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned ...

0 Ziru Zhang, et al. ∙

research

∙ 05/25/2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting

Ensembling is among the most popular tools in machine learning (ML) due ...

0 Hilaf Hasson, et al. ∙

research

∙ 05/22/2023

An Optimal Separation between Two Property Testing Models for Bounded Degree Directed Graphs

We revisit the relation between two fundamental property testing models ...

0 Pan Peng, et al. ∙

research

∙ 05/13/2023

Using a virtual reality interview simulator to explore factors influencing people's behavior

Virtual reality interview simulator (VRIS) provides an effective and man...

0 Xinyi Luo, et al. ∙

research

∙ 04/11/2023

A Deep Cybersickness Predictor through Kinematic Data with Encoded Physiological Representation

Users would experience individually different sickness symptoms during o...

0 Ruichen Li, et al. ∙

research

∙ 04/10/2023

Neural Network Predicts Ion Concentration Profiles under Nanoconfinement

Modeling the ion concentration profile in nanochannel plays an important...

0 Zhonglin Cao, et al. ∙

research

∙ 03/29/2023

Modeling online adaptive navigation in virtual environments based on PID control

It is well known that locomotion-dominated navigation tasks may highly p...

0 Yuyang Wang, et al. ∙

research

∙ 03/14/2023

Testing Causality for High Dimensional Data

Determining causal relationship between high dimensional observations ar...

0 Arun Jambulapati, et al. ∙

research

∙ 03/03/2023

Denoise Pre-training on Non-equilibrium Molecules for Accurate and Transferable Neural Potentials

Machine learning methods, particularly recent advances in equivariant gr...

0 Yuyang Wang, et al. ∙

research

∙ 01/09/2023

Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics

The diversity of workload requirements and increasing hardware heterogen...

0 George Michelogiannakis, et al. ∙

research

∙ 12/15/2022

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting

Transformer-based models have gained large popularity and demonstrated p...

0 Xiyuan Zhang, et al. ∙

research

∙ 12/07/2022

Criteria for Classifying Forecasting Methods

Classifying forecasting methods as being either of a "machine learning" ...

0 Tim Januschowski, et al. ∙

research

∙ 11/08/2022

Nimbus: Toward Speed Up Function Signature Recovery via Input Resizing and Multi-Task Learning

Function signature recovery is important for many binary analysis tasks ...

0 Yi Qian, et al. ∙

research

∙ 10/26/2022

Deep Learning Based Audio-Visual Multi-Speaker DOA Estimation Using Permutation-Free Loss Function

In this paper, we propose a deep learning based multi-speaker direction ...

0 Qing Wang, et al. ∙

research

∙ 10/25/2022

MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction

Metal-Organic Frameworks (MOFs) are materials with a high degree of poro...

0 Zhonglin Cao, et al. ∙

research

∙ 10/12/2022

Identity, Crimes, and Law Enforcement in the Metaverse

With the boom in metaverse-related projects in major areas of the public...

0 Hua Xuan Qin, et al. ∙

research

∙ 09/15/2022

On the detrimental effect of invariances in the likelihood for variational inference

Variational Bayesian posterior inference often requires simplifying appr...

8 Richard Kurle, et al. ∙

research

∙ 09/12/2022

Graph Neural Networks for Molecules

Graph neural networks (GNNs), which are capable of learning representati...

19 Yuyang Wang, et al. ∙

research

∙ 09/03/2022

TransPolymer: a Transformer-based Language Model for Polymer Property Predictions

Accurate and efficient prediction of polymer properties is of great sign...

22 Changwen Xu, et al. ∙

research

∙ 07/12/2022

Earthformer: Exploring Space-Time Transformers for Earth System Forecasting

Conventionally, Earth system (e.g., weather and climate) forecasting rel...

31 Zhihan Gao, et al. ∙

research

∙ 05/04/2022

Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction

Machine learning (ML) models have been widely successful in the predicti...

4 Rishikesh Magar, et al. ∙

research

∙ 03/17/2022

Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint of Virtual-Physical Blended Classrooms in the Metaverse Era

During the COVID-19 pandemic, most countries have experienced some form ...

0 Yuyang Wang, et al. ∙

research

∙ 02/24/2022

Robust Probabilistic Time Series Forecasting

Probabilistic time series forecasting has played critical role in decisi...

10 Taeho Yoon, et al. ∙

research

∙ 02/18/2022

Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast

Deep learning has been a prevalence in computational chemistry and widel...

2 Yuyang Wang, et al. ∙

research

∙ 02/08/2022

Graph-Relational Domain Adaptation

Existing domain adaptation methods tend to treat every domain equally an...

8 Zihao Xu, et al. ∙

research

∙ 02/01/2022

Context Uncertainty in Contextual Bandits with Applications to Recommender Systems

Recurrent neural networks have proven effective in modeling sequential u...

1 Hao Wang, et al. ∙

research

∙ 12/18/2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics

We consider the problem of probabilistic forecasting over categories wit...

8 Ke Alexander Wang, et al. ∙

research

∙ 12/14/2021

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow

The transport of traffic flow can be modeled by the advection equation. ...

0 Danielle C. Maddix, et al. ∙

research

∙ 11/30/2021

AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning

Machine learning (ML) has demonstrated the promise for accurate and effi...

1 Rishikesh Magar, et al. ∙

research

∙ 11/22/2021

Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR

We consider the framework of non-stationary Online Convex Optimization w...

2 Dheeraj Baby, et al. ∙

research

∙ 11/12/2021

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting

Quantile regression is an effective technique to quantify uncertainty, f...

10 Youngsuk Park, et al. ∙

research

∙ 10/26/2021

Deep Explicit Duration Switching Models for Time Series

Many complex time series can be effectively subdivided into distinct reg...

20 Abdul Fatir Ansari, et al. ∙

research

∙ 07/03/2021

A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification

We propose a novel neural model compression strategy combining data augm...

7 Chao-Han Huck Yang, et al. ∙

research

∙ 06/13/2021

Correcting Exposure Bias for Link Recommendation

Link prediction methods are frequently applied in recommender systems, e...

3 Shantanu Gupta, et al. ∙

research

∙ 05/18/2021

Zero-Shot Recommender Systems

Performance of recommender systems (RS) relies heavily on the amount of ...

49 Hao Ding, et al. ∙

research

∙ 02/27/2021

Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems

Millimeter wave vehicular channels exhibit structure that can be exploit...

7 Yuyang Wang, et al. ∙

research

∙ 02/19/2021

MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks

Molecular machine learning bears promise for efficient molecule property...

10 Yuyang Wang, et al. ∙

research

∙ 02/13/2021

Attention-based Domain Adaptation for Time Series Forecasting

Recent years have witnessed deep neural networks gaining increasing popu...

60 Xiaoyong Jin, et al. ∙

research

∙ 01/19/2021

Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

Two-dimensional nanomaterials, such as graphene, have been extensively s...

23 Yuyang Wang, et al. ∙

research

∙ 01/12/2021

Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization

The current design of aerodynamic shapes, like airfoils, involves comput...

13 Yuyang Wang, et al. ∙

research

∙ 11/20/2020

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

How can we learn a dynamical system to make forecasts, when some variabl...

3 Rui Wang, et al. ∙

research

∙ 10/04/2020

Intermittent Demand Forecasting with Renewal Processes

Intermittency is a common and challenging problem in demand forecasting....

4 Ali Caner Türkmen, et al. ∙

research

∙ 11/23/2019

Intermittent Demand Forecasting with Deep Renewal Processes

Intermittent demand, where demand occurrences appear sporadically in tim...

0 Ali Caner Türkmen, et al. ∙

research

∙ 06/12/2019

GluonTS: Probabilistic Time Series Models in Python

We introduce Gluon Time Series (GluonTS)[<https://gluon-ts.mxnet.io>], a...

0 Alexander Alexandrov, et al. ∙

research

∙ 05/28/2019

Deep Factors for Forecasting

Producing probabilistic forecasts for large collections of similar and/o...

6 Yuyang Wang, et al. ∙

research

∙ 11/30/2018

Deep Factors with Gaussian Processes for Forecasting

A large collection of time series poses significant challenges for class...

0 Danielle C. Maddix, et al. ∙

research

∙ 05/23/2018

MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach

Millimeter-wave communication is a challenge in the highly mobile vehicu...

0 Yuyang Wang, et al. ∙

Yuyang Wang

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