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
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Gideon Stein, Maha Shadaydeh, Joachim Denzler
ResQuNNs:Towards Enabling Deep Learning in Quantum Convolution Neural Networks
Muhammad Kashif, Muhammad Shafique
Three Decades of Activations: A Comprehensive Survey of 400 Activation Functions for Neural Networks
Vladimír Kunc, Jiří Kléma
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
S Ashwin Hebbar, Sravan Kumar Ankireddy, Hyeji Kim, Sewoong Oh, Pramod Viswanath
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas Vaughan,, Sairam Geethanath
Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities
Fodil Fadli, Yassine Himeur, Mariam Elnour, Abbes Amira
Misconduct in Post-Selections and Deep Learning
Juyang Weng
Object Detection in Thermal Images Using Deep Learning for Unmanned Aerial Vehicles
Minh Dang Tu, Kieu Trang Le, Manh Duong Phung
Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning
Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp, David F. Hanff, Judith H. Sluijter, Anne-Rose W. Schut, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grunhagen, Wiro J. Niessen, Jacob J. Visser, Stefan Klein
Tighter Bounds on the Information Bottleneck with Application to Deep Learning
Nir Weingarten, Zohar Yakhini, Moshe Butman, Ran Gilad-Bachrach
Inference Stage Denoising for Undersampled MRI Reconstruction
Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris
Accelerating Distributed Deep Learning using Lossless Homomorphic Compression
Haoyu Li, Yuchen Xu, Jiayi Chen, Rohit Dwivedula, Wenfei Wu, Keqiang He, Aditya Akella, Daehyeok Kim
Understanding Deep Learning defenses Against Adversarial Examples Through Visualizations for Dynamic Risk Assessment
Xabier Echeberria-Barrio, Amaia Gil-Lerchundi, Jon Egana-Zubia, Raul Orduna-Urrutia
A Deep Learning Method for Optimal Investment Under Relative Performance Criteria Among Heterogeneous Agents
Mathieu Laurière, Ludovic Tangpi, Xuchen Zhou