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.
3874papers
Papers - Page 41
September 17, 2024
September 16, 2024
On the effects of similarity metrics in decentralized deep learning under distributional shift
Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas GirdzijauskasTPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering
Rasoul Jafari Gohari, Laya Aliahmadipour, Ezat ValipourResearch and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning
Shuochen Bi, Yufan Lian, Ziyue Wang
September 15, 2024
Recent advances in deep learning and language models for studying the microbiome
Binghao Yan, Yunbi Nam, Lingyao Li, Rebecca A. Deek, Hongzhe Li, Siyuan MaEnhancing Lesion Segmentation in PET/CT Imaging with Deep Learning and Advanced Data Preprocessing Techniques
Jiayi Liu, Qiaoyi Xue, Youdan Feng, Tianming Xu, Kaixin Shen, Chuyun Shen, Yuhang Shi
September 14, 2024
September 13, 2024
Are Sparse Neural Networks Better Hard Sample Learners?
Qiao Xiao, Boqian Wu, Lu Yin, Christopher Neil Gadzinski, Tianjin Huang, Mykola Pechenizkiy, Decebal Constantin MocanuPINNfluence: Influence Functions for Physics-Informed Neural Networks
Jonas R. Naujoks, Aleksander Krasowski, Moritz Weckbecker, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek, René P. KlausenTrimming the Risk: Towards Reliable Continuous Training for Deep Learning Inspection Systems
Altaf Allah Abbassi, Houssem Ben Braiek, Foutse Khomh, Thomas ReidRecent Trends in Modelling the Continuous Time Series using Deep Learning: A Survey
Mansura Habiba, Barak A. Pearlmutter, Mehrdad MalekiTask-Specific Data Preparation for Deep Learning to Reconstruct Structures of Interest from Severely Truncated CBCT Data
Yixing Huang, Fuxin Fan, Ahmed Gomaa, Andreas Maier, Rainer Fietkau, Christoph Bert, Florian Putz
September 12, 2024
COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning
Zian Wang, Xinyi LuDigital Volumetric Biopsy Cores Improve Gleason Grading of Prostate Cancer Using Deep Learning
Ekaterina Redekop, Mara Pleasure, Zichen Wang, Anthony Sisk, Yang Zong, Kimberly Flores, William Speier, Corey W. ArnoldDeep Multimodal Learning with Missing Modality: A Survey
Renjie Wu, Hu Wang, Hsiang-Ting Chen, Gustavo Carneiro
September 11, 2024
Deep Learning of Dynamic Systems using System Identification Toolbox(TM)
Tianyu Dai, Khaled Aljanaideh, Rong Chen, Rajiv Singh, Alec Stothert, Lennart LjungDeep Learning for predicting rate-induced tipping
Yu Huang, Sebastian Bathiany, Peter Ashwin, Niklas BoersKKT-Informed Neural Network
Carmine Delle Femine