Deep Learning Model
Deep learning models are complex computational systems designed to learn patterns from data, achieving high accuracy in various tasks like image classification, natural language processing, and time series forecasting. Current research emphasizes improving model efficiency (e.g., through parameter reduction and optimized training algorithms), robustness (e.g., against adversarial attacks and noisy data), and interpretability (e.g., via feature attribution and visualization techniques), often employing architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformers. These advancements are driving significant impact across diverse fields, from medical diagnosis and environmental monitoring to industrial automation and personalized medicine.
Papers
From Single-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU
Patrick Rockenschaub, Adam Hilbert, Tabea Kossen, Falk von Dincklage, Vince Istvan Madai, Dietmar Frey
Neural Collapse Inspired Federated Learning with Non-iid Data
Chenxi Huang, Liang Xie, Yibo Yang, Wenxiao Wang, Binbin Lin, Deng Cai
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
Shunsuke Kitada
Uncovering Energy-Efficient Practices in Deep Learning Training: Preliminary Steps Towards Green AI
Tim Yarally, Luís Cruz, Daniel Feitosa, June Sallou, Arie van Deursen
Leaping Into Memories: Space-Time Deep Feature Synthesis
Alexandros Stergiou, Nikos Deligiannis
Operating critical machine learning models in resource constrained regimes
Raghavendra Selvan, Julian Schön, Erik B Dam
Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation
Yifan Yan, Xudong Pan, Mi Zhang, Min Yang
Diffusion-based Target Sampler for Unsupervised Domain Adaptation
Yulong Zhang, Shuhao Chen, Yu Zhang, Jiangang Lu
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri
Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation
Robert Turnbull
Plant Disease Detection using Region-Based Convolutional Neural Network
Hasin Rehana, Muhammad Ibrahim, Md. Haider Ali