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
Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
Jonas Ammeling, Moritz Hecker, Jonathan Ganz, Taryn A. Donovan, Christof A. Bertram, Katharina Breininger, Marc Aubreville
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
Nataliia Molchanova, Vatsal Raina, Andrey Malinin, Francesco La Rosa, Adrien Depeursinge, Mark Gales, Cristina Granziera, Henning Muller, Mara Graziani, Meritxell Bach Cuadra
Deep Neural Network Identification of Limnonectes Species and New Class Detection Using Image Data
Li Xu, Yili Hong, Eric P. Smith, David S. McLeod, Xinwei Deng, Laura J. Freeman
Review of AlexNet for Medical Image Classification
Wenhao Tang, Junding Sun, Shuihua Wang, Yudong Zhang
Speeding Up Optimization-based Motion Planning through Deep Learning
Johannes Tenhumberg, Darius Burschka, Berthold Bäuml
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach
Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi
Cattle Identification Using Muzzle Images and Deep Learning Techniques
G. N. Kimani, P. Oluwadara, P. Fashingabo, M. Busogi, E. Luhanga, K. Sowon, L. Chacha
Joint Alignment of Multivariate Quasi-Periodic Functional Data Using Deep Learning
Vi Thanh Pham, Jonas Bille Nielsen, Klaus Fuglsang Kofoed, Jørgen Tobias Kühl, Andreas Kryger Jensen
SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification
S. M. Nabil Ashraf, Md. Adyelullahil Mamun, Hasnat Md. Abdullah, Md. Golam Rabiul Alam
Supersampling of Data from Structured-light Scanner with Deep Learning
Martin Melicherčík, Lukáš Gajdošech, Viktor Kocur, Martin Madaras
arfpy: A python package for density estimation and generative modeling with adversarial random forests
Kristin Blesch, Marvin N. Wright
Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning
Felix den Breejen, Sangmin Bae, Stephen Cha, Tae-Young Kim, Seoung Hyun Koh, Se-Young Yun
Optical Quantum Sensing for Agnostic Environments via Deep Learning
Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin, Shanshan Zhao, Xinmei Tian, Dacheng Tao
Non-approximability of constructive global $\mathcal{L}^2$ minimizers by gradient descent in Deep Learning
Thomas Chen, Patricia Muñoz Ewald
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation
Vikas Dwivedi, Balaji Srinivasan, Ganapathy Krishnamurthi
A Survey of AI Text-to-Image and AI Text-to-Video Generators
Aditi Singh
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers
Saurav Sengupta, Donald E. Brown
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision Prototyping
Fabi Prezja
Lidar-based Norwegian tree species detection using deep learning
Martijn Vermeer, Jacob Alexander Hay, David Völgyes, Zsófia Koma, Johannes Breidenbach, Daniele Stefano Maria Fantin