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
State of the art applications of deep learning within tracking and detecting marine debris: A survey
Zoe Moorton, Dr. Zeyneb Kurt, Dr. Wai Lok Woo
Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
Andrii Kompanets, Gautam Pai, Remco Duits, Davide Leonetti, Bert Snijder
Onboard deep lossless and near-lossless predictive coding of hyperspectral images with line-based attention
Diego Valsesia, Tiziano Bianchi, Enrico Magli
A Survey on State-of-the-art Deep Learning Applications and Challenges
Mohd Halim Mohd Noor, Ayokunle Olalekan Ige
Deep Support Vectors
Junhoo Lee, Hyunho Lee, Kyomin Hwang, Nojun Kwak
Resource and Mobility Management in Hybrid LiFi and WiFi Networks: A User-Centric Learning Approach
Han Ji, Xiping Wu
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer
Dominik Müller, Philip Meyer, Lukas Rentschler, Robin Manz, Daniel Hieber, Jonas Bäcker, Samantha Cramer, Christoph Wengenmayr, Bruno Märkl, Ralf Huss, Frank Kramer, Iñaki Soto-Rey, Johannes Raffler
ModeTv2: GPU-accelerated Motion Decomposition Transformer for Pairwise Optimization in Medical Image Registration
Haiqiao Wang, Zhuoyuan Wang, Dong Ni, Yi Wang
Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models
Hanzhi Yin, Gang Cheng, Christian J. Steinmetz, Ruibin Yuan, Richard M. Stern, Roger B. Dannenberg
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Shaojie Li, Haichen Qu, Xinqi Dong, Bo Dang, Hengyi Zang, Yulu Gong
An edge detection-based deep learning approach for tear meniscus height measurement
Kesheng Wang, Kunhui Xu, Xiaoyu Chen, Chunlei He, Jianfeng Zhang, Dexing Kong, Qi Dai, Shoujun Huang
BEND: Bagging Deep Learning Training Based on Efficient Neural Network Diffusion
Jia Wei, Xingjun Zhang, Witold Pedrycz
An active learning model to classify animal species in Hong Kong
Gareth Lamb, Ching Hei Lo, Jin Wu, Calvin K. F. Lee
Your Image is My Video: Reshaping the Receptive Field via Image-To-Video Differentiable AutoAugmentation and Fusion
Sofia Casarin, Cynthia I. Ugwu, Sergio Escalera, Oswald Lanz
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul, Hans-Jürgen Profitlich, Moritz Wolf, Daniel Sonntag
Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Zeya Wang, Chenglong Ye
Deep Active Learning: A Reality Check
Edrina Gashi, Jiankang Deng, Ismail Elezi
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann, George Schwarz, Rao Muhammad Umer, Lisa Beer, Patrick Rockenschaub, Rahul Babu Shrestha, Armin Gruber, Nutan Chen, Sayedali Shetab Boushehri, Florian Buettner, Carsten Marr
A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond
Qiushi Sun, Zhirui Chen, Fangzhi Xu, Kanzhi Cheng, Chang Ma, Zhangyue Yin, Jianing Wang, Chengcheng Han, Renyu Zhu, Shuai Yuan, Qipeng Guo, Xipeng Qiu, Pengcheng Yin, Xiaoli Li, Fei Yuan, Lingpeng Kong, Xiang Li, Zhiyong Wu