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Methods/Dropout

Dropout

GeneralIntroduced 200027477 papers

Description

Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$ ). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$ ).

The idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.

Papers Using This Method

Making Language Model a Hierarchical Classifier and Generator2025-07-17 DASViT: Differentiable Architecture Search for Vision Transformer2025-07-17 Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker2025-07-16 Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows2025-07-16 DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition2025-07-16 Addressing Data Imbalance in Transformer-Based Multi-Label Emotion Detection with Weighted Loss2025-07-15 HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging2025-07-15 Langevin Flows for Modeling Neural Latent Dynamics2025-07-15 Generative Click-through Rate Prediction with Applications to Search Advertising2025-07-15 Biological Processing Units: Leveraging an Insect Connectome to Pioneer Biofidelic Neural Architectures2025-07-15 KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding2025-07-15 Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking2025-07-15 LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning2025-07-15 Overcoming catastrophic forgetting in neural networks2025-07-14 A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models2025-07-14 Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout2025-07-14 SentiDrop: A Multi Modal Machine Learning model for Predicting Dropout in Distance Learning2025-07-14 Leveraging RAG-LLMs for Urban Mobility Simulation and Analysis2025-07-14 Token Compression Meets Compact Vision Transformers: A Survey and Comparative Evaluation for Edge AI2025-07-13 Learning from Synthetic Labs: Language Models as Auction Participants2025-07-12