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
Research on Feature Extraction Data Processing System For MRI of Brain Diseases Based on Computer Deep Learning
Lingxi Xiao, Jinxin Hu, Yutian Yang, Yinqiu Feng, Zichao Li, Zexi Chen
Understanding and Diagnosing Deep Reinforcement Learning
Ezgi Korkmaz
Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study
Mrinal Kanti Dhar, Chuanbo Wang, Yash Patel, Taiyu Zhang, Jeffrey Niezgoda, Sandeep Gopalakrishnan, Keke Chen, Zeyun Yu
Anime Popularity Prediction Before Huge Investments: a Multimodal Approach Using Deep Learning
Jesús Armenta-Segura, Grigori Sidorov
Contextual Sprint Classification in Soccer Based on Deep Learning
Hyunsung Kim, Gun-Hee Joe, Jinsung Yoon, Sang-Ki Ko
Open Problem: Order Optimal Regret Bounds for Kernel-Based Reinforcement Learning
Sattar Vakili
A Dual Attention-aided DenseNet-121 for Classification of Glaucoma from Fundus Images
Soham Chakraborty, Ayush Roy, Payel Pramanik, Daria Valenkova, Ram Sarkar
ATAC-Net: Zoomed view works better for Anomaly Detection
Shaurya Gupta, Neil Gautam, Anurag Malyala
Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning
Niccolò Marini, Stefano Marchesin, Lluis Borras Ferris, Simon Püttmann, Marek Wodzinski, Riccardo Fratti, Damian Podareanu, Alessandro Caputo, Svetla Boytcheva, Simona Vatrano, Filippo Fraggetta, Iris Nagtegaal, Gianmaria Silvello, Manfredo Atzori, Henning Müller
Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
Steffen Dereich, Arnulf Jentzen, Adrian Riekert
Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning
Asaf Ben Arie, Malka Gorfine
Deep Optimal Experimental Design for Parameter Estimation Problems
Md Shahriar Rahim Siddiqui, Arman Rahmim, Eldad Haber
Optimal deep learning of holomorphic operators between Banach spaces
Ben Adcock, Nick Dexter, Sebastian Moraga
FastPersist: Accelerating Model Checkpointing in Deep Learning
Guanhua Wang, Olatunji Ruwase, Bing Xie, Yuxiong He
Fair Differentiable Neural Network Architecture Search for Long-Tailed Data with Self-Supervised Learning
Jiaming Yan
Faster Metallic Surface Defect Detection Using Deep Learning with Channel Shuffling
Siddiqui Muhammad Yasir, Hyunsik Ahn
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data
Siddiqui Muhammad Yasir, Amin Muhammad Sadiq, Hyunsik Ahn