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08/15/2023
LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation
There is an increasing interest in developing LLMs for medical diagnosis...
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08/08/2023
Learning Specialized Activation Functions for Physics-informed Neural Networks
Physics-informed neural networks (PINNs) are known to suffer from optimi...
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06/15/2023
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs
While significant progress has been made on Physics-Informed Neural Netw...
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06/14/2023
Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects
Conversational recommender systems (CRSs) have become crucial emerging r...
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05/26/2023
Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
Full waveform inversion (FWI) infers the subsurface structure informatio...
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05/19/2023
Channel Cycle Time: A New Measure of Short-term Fairness
This paper puts forth a new metric, dubbed channel cycle time, to measur...
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05/18/2023
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
We develop a tool, which we name Protoplanetary Disk Operator Network (P...
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05/08/2023
Optical Integrated Sensing and Communication
This paper explores a new paradigm of optical integrated sensing and com...
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05/05/2023
Deep Learning for Solving and Estimating Dynamic Macro-Finance Models
We develop a methodology that utilizes deep learning to simultaneously s...
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04/26/2023
Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System
Large-scale Language Models (LLMs) are constrained by their inability to...
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03/08/2023
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration
Geologic Carbon Storage (GCS) is an important technology that aims to re...
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12/13/2022
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dime...
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11/15/2022
Effective Data Sampling Strategies and Boundary Condition Constraints of Physics-Informed Neural Networks for Identifying Material Properties in Solid Mechanics
Material identification is critical for understanding the relationship b...
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10/27/2022
Aerial Manipulation Using a Novel Unmanned Aerial Vehicle Cyber-Physical System
Unmanned Aerial Vehicles(UAVs) are attaining more and more maneuverabili...
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07/29/2022
Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach
Discovering sequences with desired properties has long been an interesti...
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07/21/2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
Physics-informed neural networks (PINNs) have shown to be an effective t...
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07/13/2022
Dynamic gNodeB Sleep Control for Energy-Conserving 5G Radio Access Network
5G radio access network (RAN) is consuming much more energy than legacy ...
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06/11/2022
Sampling-based Estimation of the Number of Distinct Values in Distributed Environment
In data mining, estimating the number of distinct values (NDV) is a fund...
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06/01/2022
Digital Twin for Networking: A Data-driven Performance Modeling Perspective
Emerging technologies and applications make the network unprecedentedly ...
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04/19/2022
Importance is in your attention: agent importance prediction for autonomous driving
Trajectory prediction is an important task in autonomous driving. State-...
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04/14/2022
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimen...
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03/19/2022
Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate
We propose a novel M-estimate conjugate gradient (CG) algorithm, termed ...
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02/12/2022
MIONet: Learning multiple-input operators via tensor product
As an emerging paradigm in scientific machine learning, neural operators...
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02/03/2022
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
The dynamics of systems biological processes are usually modeled by a sy...
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12/20/2021
Nonparametric estimation of multivariate copula using empirical bayes method
In the field of finance, insurance, and system reliability, etc., it is ...
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10/19/2021
A survey on active noise control techniques – Part II: Nonlinear systems
Part I of this paper reviewed the development of the linear active noise...
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10/01/2021
A survey on active noise control techniques – Part I: Linear systems
Active noise control (ANC) is an effective way for reducing the noise le...
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03/29/2021
Modeling Bivariate Geyser Eruption System with Covariate-Adjusted Recurrent Event Process
Geyser eruption is one of the most popular signature attractions at the ...
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02/21/2021
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations
We present convergence rates of operator learning in [Chen and Chen 1995...
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02/09/2021
Physics-informed neural networks with hard constraints for inverse design
Inverse design arises in a variety of areas in engineering such as acous...
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11/05/2019
Spatial Attention for Far-field Speech Recognition with Deep Beamforming Neural Networks
In this paper, we introduce spatial attention for refining the informati...
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10/27/2019
Robots as Actors in a Film: No War, A Robot Story
Will the Third World War be fought by robots? This short film is a light...
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10/08/2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
While it is widely known that neural networks are universal approximator...
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08/26/2019
Statistical Analysis of Modern Reliability Data
Traditional reliability analysis has been using time to event data, degr...
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07/10/2019
DeepXDE: A deep learning library for solving differential equations
Deep learning has achieved remarkable success in diverse applications; h...
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07/09/2019
Gated Multiple Feedback Network for Image Super-Resolution
The rapid development of deep learning (DL) has driven single image supe...
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05/27/2019
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness
The accuracy of deep learning, i.e., deep neural networks, can be charac...
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03/15/2019
Dying ReLU and Initialization: Theory and Numerical Examples
The dying ReLU refers to the problem when ReLU neurons become inactive a...
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01/16/2019
How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition
Data competitions rely on real-time leaderboards to rank competitor entr...
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11/30/2018
Bayesian Sequential Design Based on Dual Objectives for Accelerated Life Tests
Traditional accelerated life test plans are typically based on optimizin...
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09/21/2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Physics-informed neural networks (PINNs) have recently emerged as an alt...
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08/15/2018
Collapse of Deep and Narrow Neural Nets
Recent theoretical work has demonstrated that deep neural networks have ...
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03/13/2018
Noncoherent Detection for Physical Layer Network Coding
This paper investigates noncoherent detection in a two-way relay channel...
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12/19/2017