Deep Learning Model
Deep learning models are complex computational systems designed to learn patterns from data, achieving high accuracy in various tasks like image classification, natural language processing, and time series forecasting. Current research emphasizes improving model efficiency (e.g., through parameter reduction and optimized training algorithms), robustness (e.g., against adversarial attacks and noisy data), and interpretability (e.g., via feature attribution and visualization techniques), often employing architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformers. These advancements are driving significant impact across diverse fields, from medical diagnosis and environmental monitoring to industrial automation and personalized medicine.
Papers
Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models
Mohsena Chowdhury, Tejas Vyas, Rahul Alapati, Andrés M Bur, Guanghui Wang
PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software
Wenxin Jiang, Jerin Yasmin, Jason Jones, Nicholas Synovic, Jiashen Kuo, Nathaniel Bielanski, Yuan Tian, George K. Thiruvathukal, James C. Davis
Benchmarking Transferable Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu, Huaming Chen
Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification
Haixia Liu, Tim Brailsford, James Goulding, Gavin Smith, Larry Bull
A Survey of Deep Learning and Foundation Models for Time Series Forecasting
John A. Miller, Mohammed Aldosari, Farah Saeed, Nasid Habib Barna, Subas Rana, I. Budak Arpinar, Ninghao Liu
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges
Ali Amiri, Aydin Kaya, Ali Seydi Keceli
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning
Rajaram R, Manoj Bharadhwaj, Vasan VS, Nargis Pervin
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
Hao Li, Gopi Krishnan Rajbahadur, Dayi Lin, Cor-Paul Bezemer, Zhen Ming, Jiang
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Holger Boche, Adalbert Fono, Gitta Kutyniok
Towards Scalable and Robust Model Versioning
Wenxin Ding, Arjun Nitin Bhagoji, Ben Y. Zhao, Haitao Zheng
Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
Niki Triantafyllou, Maria M. Papathanasiou
A Two-Scale Complexity Measure for Deep Learning Models
Massimiliano Datres, Gian Paolo Leonardi, Alessio Figalli, David Sutter
Trapped in texture bias? A large scale comparison of deep instance segmentation
Johannes Theodoridis, Jessica Hofmann, Johannes Maucher, Andreas Schilling
Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization
Yoonhwa Jung, Ikhyun Cho, Shun-Hsiang Hsu, Julia Hockenmaier