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
Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains
Robin Trombetta (MYRIAD), Olivier Rouvière (HCL), Carole Lartizien (MYRIAD)
Tree level change detection over Ahmedabad city using very high resolution satellite images and Deep Learning
Jai G Singla, Gautam Jaiswal
Deep Learning for Leopard Individual Identification: An Adaptive Angular Margin Approach
David Colomer Matachana
Detecting Student Disengagement in Online Classes Using Deep Learning: A Review
Ahmed Mohamed, Mostafa Ali, Shahd Ahmed, Nouran Hani, Mohammed Hisham, Meram Mahmoud
A Study of Data Augmentation Techniques to Overcome Data Scarcity in Wound Classification using Deep Learning
Harini Narayanan, Sindhu Ghanta
Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning
Hui Lin, Jiyang Li, Ramy Hussein, Xin Sui, Xiaoyu Li, Guangpu Zhu, Aggelos K. Katsaggelos, Zijing Zeng, Yelei Li
Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures
Ameya Uppina, S Navaneetha Krishnan, Talluri Krishna Sai Teja, Nikhil N Iyer, Joe Dhanith P R
Optimizing Federated Learning by Entropy-Based Client Selection
Andreas Lutz, Gabriele Steidl, Karsten Müller, Wojciech Samek
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning
Gautam Gare, Jana Armouti, Nikhil Madaan, Rohan Panda, Tom Fox, Laura Hutchins, Amita Krishnan, Ricardo Rodriguez, Bennett DeBoisblanc, Deva Ramanan, John Galeotti
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
Miria Feng, Zachary Frangella, Mert Pilanci
Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning
Simon Rampp, Manuel Milling, Andreas Triantafyllopoulos, Björn W. Schuller
Explainable few-shot learning workflow for detecting invasive and exotic tree species
Caroline M. Gevaert, Alexandra Aguiar Pedro, Ou Ku, Hao Cheng, Pranav Chandramouli, Farzaneh Dadrass Javan, Francesco Nattino, Sonja Georgievska
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
Li Li
Ratio law: mathematical descriptions for a universal relationship between AI performance and input samples
Boming Kang, Qinghua Cui
Topology and Intersection-Union Constrained Loss Function for Multi-Region Anatomical Segmentation in Ocular Images
Ruiyu Xia, Jianqiang Li, Xi Xu, Guanghui Fu
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems
Sourav Modak, Anthony Stein
Advantages of Neural Population Coding for Deep Learning
Heiko Hoffmann
Space for Improvement: Navigating the Design Space for Federated Learning in Satellite Constellations
Grace Kim, Luca Powell, Filip Svoboda, Nicholas Lane
A Novel Breast Ultrasound Image Augmentation Method Using Advanced Neural Style Transfer: An Efficient and Explainable Approach
Lipismita Panigrahi, Prianka Rani Saha, Jurdana Masuma Iqrah, Sushil Prasad