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

June 9, 2024

June 8, 2024

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
Hao Liu, Jiahui Cheng, Wenjing Liao
Deep Learning Deep Neural Network Estimation Task Average Approximation Data Distribution Deep ReLU Network Nonlinear Function Approximation Geometric Regularity

June 6, 2024

June 5, 2024

June 4, 2024

Deep Learning

Papers

General Distribution Learning: A theoretical framework for Deep Learning

Injecting Undetectable Backdoors in Obfuscated Neural Networks and Language Models

Heart Sound Segmentation Using Deep Learning Techniques

Deep Learning to Predict Glaucoma Progression using Structural Changes in the Eye

Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation

Research on Tumors Segmentation based on Image Enhancement Method

Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Error Bounds of Supervised Classification from Information-Theoretic Perspective

Towards Physically Consistent Deep Learning For Climate Model Parameterizations

Memorization in deep learning: A survey

Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models

Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection

Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers

Interactive Image Selection and Training for Brain Tumor Segmentation Network

Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI

Tabular and Deep Learning for the Whittle Index

PETRA: Parallel End-to-end Training with Reversible Architectures

Verifying the Generalization of Deep Learning to Out-of-Distribution Domains

Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising

Fruit Classification System with Deep Learning and Neural Architecture Search