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
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Amirul Islam Saimon, Emmanuel Yangue, Xiaowei Yue, Zhenyu James Kong, Chenang Liu
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms
Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
Tin Nguyen, Lam Pham, Phat Lam, Dat Ngo, Hieu Tang, Alexander Schindler
Value Prediction for Spatiotemporal Gait Data Using Deep Learning
Ryan Cavanagh, Jelena Trajkovic, Wenlu Zhang, I-Hung Khoo, Vennila Krishnan
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
Xingchen Zou, Yibo Yan, Xixuan Hao, Yuehong Hu, Haomin Wen, Erdong Liu, Junbo Zhang, Yong Li, Tianrui Li, Yu Zheng, Yuxuan Liang
SegNet: A Segmented Deep Learning based Convolutional Neural Network Approach for Drones Wildfire Detection
Aditya V. Jonnalagadda, Hashim A. Hashim
FlatNAS: optimizing Flatness in Neural Architecture Search for Out-of-Distribution Robustness
Matteo Gambella, Fabrizio Pittorino, Manuel Roveri
A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
Giuseppe Chiari, Davide Galli, Francesco Lattari, Matteo Matteucci, Davide Zoni
WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concepts
Yong Hyun Ahn, Hyeon Bae Kim, Seong Tae Kim
Physics Sensor Based Deep Learning Fall Detection System
Zeyuan Qu, Tiange Huang, Yuxin Ji, Yongjun Li
Deep Learning for 3D Human Pose Estimation and Mesh Recovery: A Survey
Yang Liu, Changzhen Qiu, Zhiyong Zhang
Training-set-free two-stage deep learning for spectroscopic data de-noising
Dongchen Huang, Junde Liu, Tian Qian, Hongming Weng
GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning
Marina Manso Jimeno, Keren Bachi, George Gardner, Yasmin L. Hurd, John Thomas Vaughan, Sairam Geethanath
GAIA: Categorical Foundations of Generative AI
Sridhar Mahadevan
A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation
Francesco Barbato, Umberto Michieli, Mehmet Kerim Yucel, Pietro Zanuttigh, Mete Ozay
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet
PiShield: A PyTorch Package for Learning with Requirements
Mihaela Cătălina Stoian, Alex Tatomir, Thomas Lukasiewicz, Eleonora Giunchiglia
Simple But Effective: Rethinking the Ability of Deep Learning in fNIRS to Exclude Abnormal Input
Zhihao Cao
Automated Discovery of Integral with Deep Learning
Xiaoxin Yin