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
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe, Jurandy Almeida
Using deep learning to construct stochastic local search SAT solvers with performance bounds
Maximilian Kramer, Paul Boes
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
Giovanni Bindi, Nils Demerlé, Rodrigo Diaz, David Genova, Aliénor Golvet, Ben Hayes, Jiawen Huang, Lele Liu, Vincent Martos, Sarah Nabi, Teresa Pelinski, Lenny Renault, Saurjya Sarkar, Pedro Sarmento, Cyrus Vahidi, Lewis Wolstanholme, Yixiao Zhang, Axel Roebel, Nick Bryan-Kinns, Jean-Louis Giavitto, Mathieu Barthet
CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration
Alper Güngör, M. Umut Bahceci, Yasin Ergen, Ahmet Sözak, O. Oner Ekiz, Tolga Yelboga, Tolga Çukur
Grassroots Operator Search for Model Edge Adaptation
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar
Dynamical Tests of a Deep-Learning Weather Prediction Model
Gregory J. Hakim, Sanjit Masanam
PAMS: Platform for Artificial Market Simulations
Masanori Hirano, Ryosuke Takata, Kiyoshi Izumi
A multimodal deep learning architecture for smoking detection with a small data approach
Robert Lakatos, Peter Pollner, Andras Hajdu, Tamas Joo
Loop Polarity Analysis to Avoid Underspecification in Deep Learning
Donald Martin,, David Kinney
Human Gait Recognition using Deep Learning: A Comprehensive Review
Muhammad Imran Sharif, Mehwish Mehmood, Muhammad Irfan Sharif, Md Palash Uddin
Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhaohui Liang, Zhiyun Xue, Sameer Antani
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Alessandro Finamore, Chao Wang, Jonatan Krolikowski, Jose M. Navarro, Fuxing Chen, Dario Rossi
A Discussion on Generalization in Next-Activity Prediction
Luka Abb, Peter Pfeiffer, Peter Fettke, Jana-Rebecca Rehse
Search and Learning for Unsupervised Text Generation
Lili Mou
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children
Yuyue Zhou, Jessica Knight, Banafshe Felfeliyan, Christopher Keen, Abhilash Rakkunedeth Hareendranathan, Jacob L. Jaremko
Unleashing the Power of Dynamic Mode Decomposition and Deep Learning for Rainfall Prediction in North-East India
Paleti Nikhil Chowdary, Sathvika P, Pranav U, Rohan S, Sowmya V, Gopalakrishnan E A, Dhanya M
Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning
Zilu Guo, Jun Du, CHin-Hui Lee
Comparative study of Deep Learning Models for Binary Classification on Combined Pulmonary Chest X-ray Dataset
Shabbir Ahmed Shuvo, Md Aminul Islam, Md. Mozammel Hoque, Rejwan Bin Sulaiman
Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning
Payam Nourizadeh, Fiona J Stevens McFadden, Will N Browne