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08/10/2023
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
We introduce AutoGluon-TimeSeries - an open-source AutoML library for pr...
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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...
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07/19/2023
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Earth system forecasting has traditionally relied on complex physical mo...
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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 ...
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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 ...
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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 ...
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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...
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04/11/2023
A Deep Cybersickness Predictor through Kinematic Data with Encoded Physiological Representation
Users would experience individually different sickness symptoms during o...
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04/10/2023
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Modeling the ion concentration profile in nanochannel plays an important...
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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...
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03/14/2023
Testing Causality for High Dimensional Data
Determining causal relationship between high dimensional observations ar...
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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...
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01/09/2023
Efficient Intra-Rack Resource Disaggregation for HPC Using Co-Packaged DWDM Photonics
The diversity of workload requirements and increasing hardware heterogen...
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12/15/2022
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting
Transformer-based models have gained large popularity and demonstrated p...
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12/07/2022
Criteria for Classifying Forecasting Methods
Classifying forecasting methods as being either of a "machine learning" ...
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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 ...
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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 ...
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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...
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10/12/2022
Identity, Crimes, and Law Enforcement in the Metaverse
With the boom in metaverse-related projects in major areas of the public...
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09/15/2022
On the detrimental effect of invariances in the likelihood for variational inference
Variational Bayesian posterior inference often requires simplifying appr...
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09/12/2022
Graph Neural Networks for Molecules
Graph neural networks (GNNs), which are capable of learning representati...
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09/03/2022
TransPolymer: a Transformer-based Language Model for Polymer Property Predictions
Accurate and efficient prediction of polymer properties is of great sign...
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07/12/2022
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting
Conventionally, Earth system (e.g., weather and climate) forecasting rel...
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05/04/2022
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Machine learning (ML) models have been widely successful in the predicti...
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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 ...
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02/24/2022
Robust Probabilistic Time Series Forecasting
Probabilistic time series forecasting has played critical role in decisi...
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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...
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02/08/2022
Graph-Relational Domain Adaptation
Existing domain adaptation methods tend to treat every domain equally an...
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02/01/2022
Context Uncertainty in Contextual Bandits with Applications to Recommender Systems
Recurrent neural networks have proven effective in modeling sequential u...
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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...
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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. ...
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11/30/2021
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Machine learning (ML) has demonstrated the promise for accurate and effi...
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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...
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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...
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10/26/2021
Deep Explicit Duration Switching Models for Time Series
Many complex time series can be effectively subdivided into distinct reg...
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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...
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06/13/2021
Correcting Exposure Bias for Link Recommendation
Link prediction methods are frequently applied in recommender systems, e...
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05/18/2021
Zero-Shot Recommender Systems
Performance of recommender systems (RS) relies heavily on the amount of ...
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02/27/2021
Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems
Millimeter wave vehicular channels exhibit structure that can be exploit...
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02/19/2021
MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks
Molecular machine learning bears promise for efficient molecule property...
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02/13/2021
Attention-based Domain Adaptation for Time Series Forecasting
Recent years have witnessed deep neural networks gaining increasing popu...
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01/19/2021
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Two-dimensional nanomaterials, such as graphene, have been extensively s...
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01/12/2021
Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization
The current design of aerodynamic shapes, like airfoils, involves comput...
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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...
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10/04/2020
Intermittent Demand Forecasting with Renewal Processes
Intermittency is a common and challenging problem in demand forecasting....
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11/23/2019
Intermittent Demand Forecasting with Deep Renewal Processes
Intermittent demand, where demand occurrences appear sporadically in tim...
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06/12/2019
GluonTS: Probabilistic Time Series Models in Python
We introduce Gluon Time Series (GluonTS)[<https://gluon-ts.mxnet.io>], a...
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05/28/2019
Deep Factors for Forecasting
Producing probabilistic forecasts for large collections of similar and/o...
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11/30/2018
Deep Factors with Gaussian Processes for Forecasting
A large collection of time series poses significant challenges for class...
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05/23/2018