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
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imaging
Iury B. de A. Santos, André C. P. L. F. de Carvalho
A Novel Bi-LSTM And Transformer Architecture For Generating Tabla Music
Roopa Mayya, Vivekanand Venkataraman, Anwesh P R, Narayana Darapaneni
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
Suman Sourabh, Murugappan Valliappan, Narayana Darapaneni, Anwesh R P
Estimation of FFR in coronary arteries with deep learning
Patryk Rygiel
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
Zi-Hao Qiu, Siqi Guo, Mao Xu, Tuo Zhao, Lijun Zhang, Tianbao Yang
Deep-learning Segmentation of Small Volumes in CT images for Radiotherapy Treatment Planning
Jianxin Zhou, Kadishe Fejza, Massimiliano Salvatori, Daniele Della Latta, Gregory M. Hermann, Angela Di Fulvio
Noisy Label Processing for Classification: A Survey
Mengting Li, Chuang Zhu
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
Romain Egele, Felix Mohr, Tom Viering, Prasanna Balaprakash
Model Selection with Model Zoo via Graph Learning
Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
Deep Learning for Satellite Image Time Series Analysis: A Review
Lynn Miller, Charlotte Pelletier, Geoffrey I. Webb
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI
Maryam Ahmed, Tooba Bibi, Rizwan Ahmed Khan, Sidra Nasir
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
Dongang Wang, Peilin Liu, Hengrui Wang, Heidi Beadnall, Kain Kyle, Linda Ly, Mariano Cabezas, Geng Zhan, Ryan Sullivan, Weidong Cai, Wanli Ouyang, Fernando Calamante, Michael Barnett, Chenyu Wang
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
Spyridon Chavlis, Panayiota Poirazi
Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
Zakaria Elabid, Daniel Busby, Abdenour Hadid
Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection
Litao Li, Steven H. H. Ding, Andrew Walenstein, Philippe Charland, Benjamin C. M. Fung
Deep Image Composition Meets Image Forgery
Eren Tahir, Mert Bal
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
Poulami Sinhamahapatra, Suprosanna Shit, Anjany Sekuboyina, Malek Husseini, David Schinz, Nicolas Lenhart, Joern Menze, Jan Kirschke, Karsten Roscher, Stephan Guennemann
Can We Understand Plasticity Through Neural Collapse?
Guglielmo Bonifazi, Iason Chalas, Gian Hess, Jakub Łucki
Fusing Multi-sensor Input with State Information on TinyML Brains for Autonomous Nano-drones
Luca Crupi, Elia Cereda, Daniele Palossi