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
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Yichao Wang, Bo Chen, Wanyu Wang, Huifeng Guo, Ruiming Tang
Equity through Access: A Case for Small-scale Deep Learning
Raghavendra Selvan, Bob Pepin, Christian Igel, Gabrielle Samuel, Erik B Dam
Geometric Constraints in Deep Learning Frameworks: A Survey
Vibhas K Vats, David J Crandall
Sim2Real in Reconstructive Spectroscopy: Deep Learning with Augmented Device-Informed Data Simulation
Jiyi Chen, Pengyu Li, Yutong Wang, Pei-Cheng Ku, Qing Qu
A Systematic Review of Generalization Research in Medical Image Classification
Sarah Matta, Mathieu Lamard, Philippe Zhang, Alexandre Le Guilcher, Laurent Borderie, Béatrice Cochener, Gwenolé Quellec
Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
Luhuan Wu, Sinead Williamson
Language Evolution with Deep Learning
Mathieu Rita, Paul Michel, Rahma Chaabouni, Olivier Pietquin, Emmanuel Dupoux, Florian Strub
Deep learning automates Cobb angle measurement compared with multi-expert observers
Keyu Li, Hanxue Gu, Roy Colglazier, Robert Lark, Elizabeth Hubbard, Robert French, Denise Smith, Jikai Zhang, Erin McCrum, Anthony Catanzano, Joseph Cao, Leah Waldman, Maciej A. Mazurowski, Benjamin Alman
GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects
Sungphill Moon, Hyeontae Son, Dongcheol Hur, Sangwook Kim
Automated data processing and feature engineering for deep learning and big data applications: a survey
Alhassan Mumuni, Fuseini Mumuni
Advanced Knowledge Extraction of Physical Design Drawings, Translation and conversion to CAD formats using Deep Learning
Jesher Joshua M, Ragav V, Syed Ibrahim S P
CBR -- Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-distribution
Amir Lukach, Ran Dubin, Amit Dvir, Chen Hajaj
A lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
Mahdie Ahmadi, Nader Karimi, Shadrokh Samavi
Bridging Expert Knowledge with Deep Learning Techniques for Just-In-Time Defect Prediction
Xin Zhou, DongGyun Han, David Lo
Understanding the Double Descent Phenomenon in Deep Learning
Marc Lafon, Alexandre Thomas
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
Lukas Rauch, Raphael Schwinger, Moritz Wirth, René Heinrich, Denis Huseljic, Marek Herde, Jonas Lange, Stefan Kahl, Bernhard Sick, Sven Tomforde, Christoph Scholz