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
Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning
Lorenzo Noci, Alexandru Meterez, Thomas Hofmann, Antonio Orvieto
Deep Learning Based Named Entity Recognition Models for Recipes
Mansi Goel, Ayush Agarwal, Shubham Agrawal, Janak Kapuriya, Akhil Vamshi Konam, Rishabh Gupta, Shrey Rastogi, Niharika, Ganesh Bagler
Outdoor Environment Reconstruction with Deep Learning on Radio Propagation Paths
Hrant Khachatrian, Rafayel Mkrtchyan, Theofanis P. Raptis
How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation
André Ferreira, Naida Solak, Jianning Li, Philipp Dammann, Jens Kleesiek, Victor Alves, Jan Egger
Prioritizing Informative Features and Examples for Deep Learning from Noisy Data
Dongmin Park
Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework
Tosin Ige, Christopher Kiekintveld, Aritran Piplai
Inpainting Computational Fluid Dynamics with Deep Learning
Dule Shu, Wilson Zhen, Zijie Li, Amir Barati Farimani
CARTE: Pretraining and Transfer for Tabular Learning
Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux
Video-Based Autism Detection with Deep Learning
M. Serna-Aguilera, X. B. Nguyen, A. Singh, L. Rockers, S. Park, L. Neely, H. Seo, K. Luu
Enhancing Kubernetes Automated Scheduling with Deep Learning and Reinforcement Techniques for Large-Scale Cloud Computing Optimization
Zheng Xu, Yulu Gong, Yanlin Zhou, Qiaozhi Bao, Wenpin Qian
Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects
Han Wang, Sijia Yu, Chunyang Chen, Burak Turhan, Xiaodong Zhu
On the Feasibility of Deep Learning Classification from Raw Signal Data in Radiology, Ultrasonography and Electrophysiology
Szilard Enyedi
Deep Learning Approaches for Improving Question Answering Systems in Hepatocellular Carcinoma Research
Shuning Huo, Yafei Xiang, Hanyi Yu, Mengran Zhu, Yulu Gong
Emotion Classification in Short English Texts using Deep Learning Techniques
Siddhanth Bhat
A Step-by-step Introduction to the Implementation of Automatic Differentiation
Yu-Hsueh Fang, He-Zhe Lin, Jie-Jyun Liu, Chih-Jen Lin
Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis
Qishuo Cheng, Le Yang, Jiajian Zheng, Miao Tian, Duan Xin
LiMAML: Personalization of Deep Recommender Models via Meta Learning
Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang, Zeinab S. Jalali, Varun Bharill, Yunbo Ouyang, Aastha Nigam, Divya Venugopalan, Aman Gupta, Fedor Borisyuk, Sathiya Keerthi, Ajith Muralidharan
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Abolfazl Younesi, Mohsen Ansari, MohammadAmin Fazli, Alireza Ejlali, Muhammad Shafique, Jörg Henkel
Multi-Objective Learning for Deformable Image Registration
Monika Grewal, Henrike Westerveld, Peter A. N. Bosman, Tanja Alderliesten