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 Unified Scheme of ResNet and Softmax
Zhao Song, Weixin Wang, Junze Yin
A mirror-Unet architecture for PET/CT lesion segmentation
Yamila Rotstein Habarnau, Mauro Namías
Predicting Temperature of Major Cities Using Machine Learning and Deep Learning
Wasiou Jaharabi, MD Ibrahim Al Hossain, Rownak Tahmid, Md. Zuhayer Islam, T. M. Saad Rayhan
Class Attendance System in Education with Deep Learning Method
Hüdaverdi Demir, Serkan Savaş
Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning
Lukas AW Gemein, Robin T Schirrmeister, Joschka Boedecker, Tonio Ball
Scalable Semantic 3D Mapping of Coral Reefs with Deep Learning
Jonathan Sauder, Guilhem Banc-Prandi, Anders Meibom, Devis Tuia
Are Deep Learning Classification Results Obtained on CT Scans Fair and Interpretable?
Mohamad M. A. Ashames, Ahmet Demir, Omer N. Gerek, Mehmet Fidan, M. Bilginer Gulmezoglu, Semih Ergin, Mehmet Koc, Atalay Barkana, Cuneyt Calisir
Classification of Alzheimers Disease with Deep Learning on Eye-tracking Data
Harshinee Sriram, Cristina Conati, Thalia Field
Impact of architecture on robustness and interpretability of multispectral deep neural networks
Charles Godfrey, Elise Bishoff, Myles McKay, Eleanor Byler
Improving VTE Identification through Adaptive NLP Model Selection and Clinical Expert Rule-based Classifier from Radiology Reports
Jamie Deng, Yusen Wu, Hilary Hayssen, Brain Englum, Aman Kankaria, Minerva Mayorga-Carlin, Shalini Sahoo, John Sorkin, Brajesh Lal, Yelena Yesha, Phuong Nguyen
Soft Merging: A Flexible and Robust Soft Model Merging Approach for Enhanced Neural Network Performance
Hao Chen, Yusen Wu, Phuong Nguyen, Chao Liu, Yelena Yesha
Brain Tumor Detection Using Deep Learning Approaches
Razia Sultana Misu
An Efficient Consolidation of Word Embedding and Deep Learning Techniques for Classifying Anticancer Peptides: FastText+BiLSTM
Onur Karakaya, Zeynep Hilal Kilimci
MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models
Yidong Liu, FuKai Shang, Fang Wang, Rui Xu, Jun Wang, Wei Li, Yao Li, Conghui He