Convolutional Neural Network

Convolutional Neural Networks (CNNs) are a class of deep learning models designed for processing grid-like data, excelling in image analysis and related tasks. Current research focuses on improving CNN efficiency and robustness, exploring architectures like EfficientNet and Swin Transformers, as well as novel approaches such as Mamba models to address limitations in computational cost and long-range dependency capture. This active field of research has significant implications across diverse applications, including medical image analysis (e.g., cancer detection, Alzheimer's diagnosis), damage assessment, and art forgery detection, demonstrating the power of CNNs for automating complex visual tasks.

Papers

February 12, 2024

Convolutional Neural Networks for signal detection in real LIGO data
Ondřej Zelenka, Bernd Brügmann, Frank Ohme
Machine Learning Convolutional Neural Network Machine Learning Algorithm Gravitational Wave Signal Detection Learning Search

February 11, 2024

Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks
Muqun Niu, Yuan Ren, Boyu Li, Chenchen Ding
Convolutional Neural Network Quantization Aware Training Low Bit Quantization Structural Re Parameterization Non Uniform Quantization

February 10, 2024

Generalization Error of Graph Neural Networks in the Mean-field Regime
Gholamali Aminian, Yixuan He, Gesine Reinert, Łukasz Szpruch, Samuel N. Cohen
Neural Network Convolutional Neural Network Graph Neural Network Mean Field Generalization Error Parameterized Regime Graph Sample

February 9, 2024

February 8, 2024

February 7, 2024

February 5, 2024

February 4, 2024

February 3, 2024

Convolutional Neural Network

Papers

Convolutional Neural Networks for signal detection in real LIGO data

Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks

Generalization Error of Graph Neural Networks in the Mean-field Regime

Flexible infinite-width graph convolutional networks and the importance of representation learning

Multi-class real-time crash risk forecasting using convolutional neural network: Istanbul case study

Multispecies bird sound recognition using a fully convolutional neural network

Spiking Neural Network Enhanced Hand Gesture Recognition Using Low-Cost Single-photon Avalanche Diode Array

Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation

Color Recognition in Challenging Lighting Environments: CNN Approach

Use of Multi-CNNs for Section Analysis in Static Malware Detection

Asymptotic generalization error of a single-layer graph convolutional network

Pre-training of Lightweight Vision Transformers on Small Datasets with Minimally Scaled Images

Improving Pediatric Low-Grade Neuroepithelial Tumors Molecular Subtype Identification Using a Novel AUROC Loss Function for Convolutional Neural Networks

Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining

Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey

NOAH: Learning Pairwise Object Category Attentions for Image Classification

Spin: An Efficient Secure Computation Framework with GPU Acceleration

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN

Diabetes detection using deep learning techniques with oversampling and feature augmentation

Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network