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Found 17 papers, 4 papers with code
GNOT: A General Neural Operator Transformer for Operator Learning
However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
Deep learning for full-field ultrasonic characterization
This study takes advantage of recent advances in machine learning to establish a physics-based data analytic platform for distributed reconstruction of mechanical properties in layered components from full waveform data.
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching.
DABERT: Dual Attention Enhanced BERT for Semantic Matching
To alleviate this problem, we propose a novel Dual Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture fine-grained differences in sentence pairs.
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients
In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints.
Single-scatter channel impulse response model of non-line-of-sight ultraviolet communications
Previous studies on the temporal characteristics of single-scatter transmission in non-line-of-sight (NLOS) ultraviolet communications (UVC) were based on the prolate-spheroidal coordinate system.
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing
Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs.
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Transition behavior of the seizure dynamics modulated by the astrocyte inositol triphosphate noise
Our simulation results show that the increase of the IP3 noise intensity induces the depolarization-block epileptic seizures together with an increase in neuronal firing frequency.
Binarized Aggregated Network with Quantization: Flexible Deep Learning Deployment for CSI Feedback in Massive MIMO System
Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system.
On eigenvalues of the Brownian sheet matrix
We derive a system of stochastic partial differential equations satisfied by the eigenvalues of the symmetric matrix whose entries are the Brownian sheets.
Probability
Skorohod and Stratonovich integrals for controlled processes
Given a continuous Gaussian process $x$ which gives rise to a $p$-geometric rough path for $p\in (2, 3)$, and a general continuous process $y$ controlled by $x$, under proper conditions we establish the relationship between the Skorohod integral $\int_0^t y_s {\mathrm{d}}^\diamond x_s$ and the Stratonovich integral $\int_0^t y_s {\mathrm{d}} {\mathbf x}_s$.
Probability
Aggregated Network for Massive MIMO CSI Feedback
In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station.
Binary Neural Network Aided CSI Feedback in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain.
Multi-resolution CSI Feedback with deep learning in Massive MIMO System
In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS).
Joint Transceiver Optimization for Wireless Communication PHY with Convolutional Neural Network
The simulation results show that the performance of neural network based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate (BER) under fading channels.
