Search Results for author:
Found 25 papers, 5 papers with code
A circular microphone array with virtual microphones based on acoustics-informed neural networks
Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions.
Sound Field Reconstruction Using a Compact Acoustics-informed Neural Network
Sound field reconstruction (SFR) augments the information of a sound field captured by a microphone array.
Retrosynthesis Prediction via Search in (Hyper) Graph
As a semi-template-based method RetroSiG has several advantages.
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning
To tackle these challenges, this study proposes an integrated drill boom control method based on Reinforcement Learning (RL).
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation
In this paper, we introduce a novel ViT-based WSSS method named top-K pooling with patch contrastive learning (TKP-PCL), which employs a top-K pooling layer to alleviate the limitations of previous max pooling selection.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
GPT-Prompt Controlled Diffusion for Weakly-Supervised Semantic Segmentation
In this process, the existing images and image-level labels provide the necessary control information, where GPT is employed to enrich the prompts, leading to the generation of diverse backgrounds.
An Active Noise Control System Based on Soundfield Interpolation Using a Physics-informed Neural Network
An ANC system is designed to take advantage of the interpolated signal to reduce noise signal within the ROI.
Head-Related Transfer Function Interpolation with a Spherical CNN
The proposed method realizes the convolution process by decomposing and reconstructing HRTF through the Spherical Harmonics (SHs).
Use neural networks to recognize students' handwritten letters and incorrect symbols
Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task.
Circumvent spherical Bessel function nulls for open sphere microphone arrays with physics informed neural network
A PINN models the measurement of an OSMA and predicts the sound field on another sphere whose radius is different from that of the OSMA.
Spatial Upsampling of Head-Related Transfer Functions Using a Physics-Informed Neural Network
Head-related transfer function (HRTF) capture the information that a person uses to localize sound sources in space, and thus is crucial for creating personalized virtual acoustic experiences.
Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Network
To overcome this challenge, this paper proposes a method for sound field estimation based on a physics-informed neural network.
Radar-STDA: A High-Performance Spatial-Temporal Denoising Autoencoder for Interference Mitigation of FMCW Radars
Therefore, interference mitigation is of great significance for millimeter-wave radar-based target detection.
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning
The critical insight in this framework is that the single or multiple reaction center must be a node-induced subgraph of the molecular product graph.
Jet tagging algorithm of graph network with HaarPooling message passing
In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet).
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS).
SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images
As a result, deep learning methods have not been fully used in airport detection tasks.
CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer
Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery.
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks
Self-supervised learning has gradually emerged as a powerful technique for graph representation learning.
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality
Moreover, we develop a generalized form of the softmax function to effectively implement maximum likelihood estimation in an end-to-end manner.
A time-domain nearfield frequency-invariant beamforming method
The time-domain implementation makes the beamformer output suitable for further use by real-time applications, the nearfield focusing enables the beamforming method to suppress an interference even if it is in the same direction as the target source, and the frequency-invariant beampattern makes the beamforming method suitable for enhancing the target source over a broad frequency band.
Sub-GMN: The Neural Subgraph Matching Network Model
Comparing with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage.
A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image
To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details.
Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification
In addition, a dual-branch convolutional neural network (CNN) classification method is designed in combination with the global information to mine the pixel features of the image.
Relaxed Actor-Critic with Convergence Guarantees for Continuous-Time Optimal Control of Nonlinear Systems
The warm-up phase minimizes the square of the Hamiltonian to achieve admissibility, while the generalized policy iteration phase relaxes the update termination conditions for faster convergence.
