Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)
Sabit Ahamed Preanto (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md. Taimur Ahad (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Yousuf Rayhan Emon (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Sumaya Mustofa (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md Alamin (4IR Research Cell Daffodil International University, Dhaka, Bangladesh)
NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods
William Van Woensel, Soroor Motie
Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
David Millard, Arielle Carr, Stéphane Gaudreault
Catch Me if You Can: Detecting Unauthorized Data Use in Deep Learning Models
Zitao Chen, Karthik Pattabiraman
Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines
Yining Chen, Jianqiang Li, Changwei Song, Qing Zhao, Yongsheng Tong, Guanghui Fu
Pioneering Precision in Lumbar Spine MRI Segmentation with Advanced Deep Learning and Data Enhancement
Istiak Ahmed, Md. Tanzim Hossain, Md. Zahirul Islam Nahid, Kazi Shahriar Sanjid, Md. Shakib Shahariar Junayed, M. Monir Uddin, Mohammad Monirujjaman Khan
DeepFM-Crispr: Prediction of CRISPR On-Target Effects via Deep Learning
Condy Bao, Fuxiao Liu
NeurLZ: On Enhancing Lossy Compression Performance based on Error-Controlled Neural Learning for Scientific Data
Wenqi Jia, Youyuan Liu, Zhewen Hu, Jinzhen Wang, Boyuan Zhang, Wei Niu, Junzhou Huang, Stavros Kalafatis, Sian Jin, Miao Yin
Robust Real-time Segmentation of Bio-Morphological Features in Human Cherenkov Imaging during Radiotherapy via Deep Learning
Shiru Wang, Yao Chen, Lesley A. Jarvis, Yucheng Tang, David J. Gladstone, Kimberley S. Samkoe, Brian W. Pogue, Petr Bruza, Rongxiao Zhang
Deep Learning for Video Anomaly Detection: A Review
Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Peng Wang, Yanning Zhang
A Quantitative Approach for Evaluating Disease Focus and Interpretability of Deep Learning Models for Alzheimer's Disease Classification
Thomas Yu Chow Tam, Litian Liang, Ke Chen, Haohan Wang, Wei Wu
Adaptative Context Normalization: A Boost for Deep Learning in Image Processing
Bilal Faye, Hanane Azzag, Mustapha Lebbah, Djamel Bouchaffra
Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training
Yuhan Ma, Dan Sun, Erdi Gao, Ningjing Sang, Iris Li, Guanming Huang
TropNNC: Structured Neural Network Compression Using Tropical Geometry
Konstantinos Fotopoulos, Petros Maragos, Panagiotis Misiakos
TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation
Shahzaib Iqbal, Tariq M. Khan, Syed S. Naqvi, Asim Naveed, Erik Meijering
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning Models
Qi Ju, Falin Hei, Zhemei Fang, Yunfeng Luo
Look Into the LITE in Deep Learning for Time Series Classification
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
June Moh Goo, Xenios Milidonis, Alessandro Artusi, Jan Boehm, Carlo Ciliberto
Deep Learning Meets Satellite Images -- An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images
Shuang Song, Luca Morelli, Xinyi Wu, Rongjun Qin, Hessah Albanwan, Fabio Remondino