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
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir
Hybrid Dynamic Pruning: A Pathway to Efficient Transformer Inference
Ghadeer Jaradat, Mohammed Tolba, Ghada Alsuhli, Hani Saleh, Mahmoud Al-Qutayri, Thanos Stouraitis, Baker Mohammad
Virtual Gram staining of label-free bacteria using darkfield microscopy and deep learning
Cagatay Isil, Hatice Ceylan Koydemir, Merve Eryilmaz, Kevin de Haan, Nir Pillar, Koray Mentesoglu, Aras Firat Unal, Yair Rivenson, Sukantha Chandrasekaran, Omai B. Garner, Aydogan Ozcan
Quality Scalable Quantization Methodology for Deep Learning on Edge
Salman Abdul Khaliq, Rehan Hafiz
A Dual-Attention Aware Deep Convolutional Neural Network for Early Alzheimer's Detection
Pandiyaraju V, Shravan Venkatraman, Abeshek A, Aravintakshan S A, Pavan Kumar S, Kannan A
Employing Sentence Space Embedding for Classification of Data Stream from Fake News Domain
Paweł Zyblewski, Jakub Klikowski, Weronika Borek-Marciniec, Paweł Ksieniewicz
Mammographic Breast Positioning Assessment via Deep Learning
Toygar Tanyel, Nurper Denizoglu, Mustafa Ege Seker, Deniz Alis, Esma Cerekci, Ercan Karaarslan, Erkin Aribal, Ilkay Oksuz
Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
Phillip Mueller, Lars Mikelsons
Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves
Tayssir Bouraffa, Elias Kjellberg Carlson, Erik Wessman, Ali Nouri, Pierre Lamart, Christian Berger
Deep ContourFlow: Advancing Active Contours with Deep Learning
Antoine Habis, Vannary Meas-Yedid, Elsa Angelini, Jean-Christophe Olivo-Marin
Deep-TEMPEST: Using Deep Learning to Eavesdrop on HDMI from its Unintended Electromagnetic Emanations
Santiago Fernández, Emilio Martínez, Gabriel Varela, Pablo Musé, Federico Larroca
ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers
Ding Zhu, Vishnu Kabir Chhabra, Mohammad Mahdi Khalili
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Laniqng Guo, Chong Wang, Yufei Wang, Siyu Huang, Wenhan Yang, Alex C. Kot, Bihan Wen
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani, Jean-Charles Verdier, Pierre-Martin Tardif, Marc Frappier, Froduald Kabanza
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Linglong Qian, Tao Wang, Jun Wang, Hugh Logan Ellis, Robin Mitra, Richard Dobson, Zina Ibrahim
Approximating G(t)/GI/1 queues with deep learning
Eliran Sherzer, Opher Baron, Dmitry Krass, Yehezkel Resheff
Improving Dental Diagnostics: Enhanced Convolution with Spatial Attention Mechanism
Shahriar Rezaie, Neda Saberitabar, Elnaz Salehi