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
Farthest Greedy Path Sampling for Two-shot Recommender Search
Yufan Cao, Tunhou Zhang, Wei Wen, Feng Yan, Hai Li, Yiran Chen
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
Extracting Entities of Interest from Comparative Product Reviews
Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak
Low-Dose CT Image Enhancement Using Deep Learning
A. Demir, M. M. A. Shames, O. N. Gerek, S. Ergin, M. Fidan, M. Koc, M. B. Gulmezoglu, A. Barkana, C. Calisir
Data Market Design through Deep Learning
Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges
Zhe Jiang
Density Estimation for Entry Guidance Problems using Deep Learning
Jens A. Rataczak, Davide Amato, Jay W. McMahon
A Principled Hierarchical Deep Learning Approach to Joint Image Compression and Classification
Siyu Qi, Achintha Wijesinghe, Lahiru D. Chamain, Zhi Ding
Approximation Theory, Computing, and Deep Learning on the Wasserstein Space
Massimo Fornasier, Pascal Heid, Giacomo Enrico Sodini
Are Natural Domain Foundation Models Useful for Medical Image Classification?
Joana Palés Huix, Adithya Raju Ganeshan, Johan Fredin Haslum, Magnus Söderberg, Christos Matsoukas, Kevin Smith
Inverse folding for antibody sequence design using deep learning
Frédéric A. Dreyer, Daniel Cutting, Constantin Schneider, Henry Kenlay, Charlotte M. Deane
Deep Learning for Visual Navigation of Underwater Robots
M. Sunbeam
A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma
Navodini Wijethilake, Steve Connor, Anna Oviedova, Rebecca Burger, Tom Vercauteren, Jonathan Shapey
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation
Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu, Gulsen Taskin, Ujjwal Verma, Francescopaolo Sica, Ronny Hansch
Predicting recovery following stroke: deep learning, multimodal data and feature selection using explainable AI
Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M. H. Hope, Cathy J. Price, Howard Bowman
Good Tools are Half the Work: Tool Usage in Deep Learning Projects
Evangelia Panourgia, Theodoros Plessas, Ilias Balampanis, Diomidis Spinellis
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Zhen Qian, Min Chen, Zhuo Sun, Fan Zhang, Qingsong Xu, Jinzhao Guo, Zhiwei Xie, Zhixin Zhang
Emotion-Oriented Behavior Model Using Deep Learning
Muhammad Arslan Raza, Muhammad Shoaib Farooq, Adel Khelifi, Atif Alvi
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
Derick Nganyu Tanyu, Jianfeng Ning, Andreas Hauptmann, Bangti Jin, Peter Maass