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
RelBench: A Benchmark for Deep Learning on Relational Databases
Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec
Adaptive Soft Error Protection for Deep Learning
Xinghua Xue, Cheng Liu
Deep Learning Based Crime Prediction Models: Experiments and Analysis
Rittik Basak Utsha, Muhtasim Noor Alif, Yeasir Rayhan, Tanzima Hashem, Mohammad Eunus Ali
A Survey of Malware Detection Using Deep Learning
Ahmed Bensaoud, Jugal Kalita, Mahmoud Bensaoud
Using deep learning to enhance electronic service quality: Application to real estate websites
Samaa Elnagar
Mathematical theory of deep learning
Philipp Petersen, Jakob Zech
AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction
Chunan Liu, Lilian Denzler, Yihong Chen, Andrew Martin, Brooks Paige
SR-CurvANN: Advancing 3D Surface Reconstruction through Curvature-Aware Neural Networks
Marina Hernández-Bautista, Francisco J. Melero
EllipBench: A Large-scale Benchmark for Machine-learning based Ellipsometry Modeling
Yiming Ma, Xinjie Li, Xin Sun, Zhiyong Wang, Lionel Z. Wang
Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation
Saideep Kilaru, Kothamasu Jayachandra, Tanishka Yagneshwar, Suchi Kumari
Hopfield Networks for Asset Allocation
Carlo Nicolini, Monisha Gopalan, Jacopo Staiano, Bruno Lepri
Reporting and Analysing the Environmental Impact of Language Models on the Example of Commonsense Question Answering with External Knowledge
Aida Usmanova, Junbo Huang, Debayan Banerjee, Ricardo Usbeck
Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets
Aleksander Ogonowski, Michał Żebrowski, Arkadiusz Ćwiek, Tobiasz Jarosiewicz, Konrad Klimaszewski, Adam Padee, Piotr Wasiuk, Michał Wójcik
Establishing Truly Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learning
Tianhang Nan, Yong Ding, Hao Quan, Deliang Li, Mingchen Zou, Xiaoyu Cui
An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation
Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
Deep Learning for Pancreas Segmentation: a Systematic Review
Andrea Moglia, Matteo Cavicchioli, Luca Mainardi, Pietro Cerveri
Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review
Alexander Rombach, Peter Fettke
Estimating Environmental Cost Throughout Model's Adaptive Life Cycle
Vishwesh Sangarya, Richard Bradford, Jung-Eun Kim