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
Image-to-Joint Inverse Kinematic of a Supportive Continuum Arm Using Deep Learning
Shayan Sepahvand, Guanghui Wang, Farrokh Janabi-Sharifi
Effects of Dataset Sampling Rate for Noise Cancellation through Deep Learning
Brandon Colelough, Andrew Zheng
A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning
Xiaofeng Cong, Yu Zhao, Jie Gui, Junming Hou, Dacheng Tao
Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari
BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction
Bridget T. McInnes, Jiawei Tang, Darshini Mahendran, Mai H. Nguyen
Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning
Jaydip Sen, Hetvi Waghela, Sneha Rakshit
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 Pandemic
Ioanna Gogou, Dimitrios Koutsomitropoulos
Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis
Jaskaran Singh Walia, Pavithra L K, Kesar Mehta, Shivram Harshavardhana, Nandini Tyagi
Exploring Loss Design Techniques For Decision Tree Robustness To Label Noise
Lukasz Sztukiewicz, Jack Henry Good, Artur Dubrawski
Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese
Marcelo Matheus Gauy, Larissa Cristina Berti, Arnaldo Cândido, Augusto Camargo Neto, Alfredo Goldman, Anna Sara Shafferman Levin, Marcus Martins, Beatriz Raposo de Medeiros, Marcelo Queiroz, Ester Cerdeira Sabino, Flaviane Romani Fernandes Svartman, Marcelo Finger
Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory
Nikola Zubić, Federico Soldá, Aurelio Sulser, Davide Scaramuzza
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, Marc Langheinrich
BOLD: Boolean Logic Deep Learning
Van Minh Nguyen, Cristian Ocampo, Aymen Askri, Louis Leconte, Ba-Hien Tran
Apply Distributed CNN on Genomics to accelerate Transcription-Factor TAL1 Motif Prediction
Tasnim Assali, Zayneb Trabelsi Ayoub, Sofiane Ouni
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets
Hyekyoung Hwang, Jitae Shin
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model Integrating CNN, LSTM, and GRU
Md Abrar Jahin, Asef Shahriar, Md Al Amin
Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout
Kristóf Németh, Dániel Hadházi