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
Deep Learning Based Simulators for the Phosphorus Removal Process Control in Wastewater Treatment via Deep Reinforcement Learning Algorithms
Esmaeel Mohammadi, Mikkel Stokholm-Bjerregaard, Aviaja Anna Hansen, Per Halkjær Nielsen, Daniel Ortiz-Arroyo, Petar Durdevic
A Review of Deep Learning Methods for Photoplethysmography Data
Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong
Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling
Nicholas Galioto, Harsh Sharma, Boris Kramer, Alex Arkady Gorodetsky
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy
Álvaro Sabater-Gárriz, F. Xavier Gaya-Morey, José María Buades-Rubio, Cristina Manresa Yee, Pedro Montoya, Inmaculada Riquelme
DeepCERES: A Deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI
Sergio Morell-Ortega, Marina Ruiz-Perez, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Maria de la Iglesia-Vaya, Gwenaelle Catheline, Pierrick Coupé, José V. Manjón
Assessing the Efficacy of Deep Learning Approaches for Facial Expression Recognition in Individuals with Intellectual Disabilities
F. Xavier Gaya-Morey, Silvia Ramis, Jose M. Buades-Rubio, Cristina Manresa-Yee
Unveiling the Human-like Similarities of Automatic Facial Expression Recognition: An Empirical Exploration through Explainable AI
F. Xavier Gaya-Morey, Silvia Ramis-Guarinos, Cristina Manresa-Yee, Jose M. Buades-Rubio
Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review
F. Xavier Gaya-Morey, Cristina Manresa-Yee, Jose M. Buades-Rubio
Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
Tianlun Hu, Qi Liao, Qiang Liu, Antonio Massaro, Georg Carle
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders
Ruixuan Sun, Xinyi Wu, Avinash Akella, Ruoyan Kong, Bart Knijnenburg, Joseph A. Konstan
Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training
Rongwu Xu, Zhixuan Fang
Frost Prediction Using Machine Learning Methods in Fars Province
Milad Barooni, Koorush Ziarati, Ali Barooni
Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach
Md Zubair, Md. Shahidul Salim, Mehrab Mustafy Rahman, Mohammad Jahid Ibna Basher, Shahin Imran, Iqbal H. Sarker
ANNA: A Deep Learning Based Dataset in Heterogeneous Traffic for Autonomous Vehicles
Mahedi Kamal, Tasnim Fariha, Afrina Kabir Zinia, Md. Abu Syed, Fahim Hasan Khan, Md. Mahbubur Rahman
Advancements in eHealth Data Analytics through Natural Language Processing and Deep Learning
Elena-Simona Apostol, Ciprian-Octavian Truică
Neglected Hessian component explains mysteries in Sharpness regularization
Yann N. Dauphin, Atish Agarwala, Hossein Mobahi
Early alignment in two-layer networks training is a two-edged sword
Etienne Boursier, Nicolas Flammarion
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
Bruna Junqueira, Bruno Aristimunha, Sylvain Chevallier, Raphael Y. de Camargo
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
Shashwat Khandelwal, Shreejith Shanker