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

May 12, 2023

Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era
Meng Zheng
Deep Learning Model Deep Learning Application Programming Assistance Model Driven Deep Learning Era Single Atom Model Paradigm

May 9, 2023

May 8, 2023

May 7, 2023

Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism
Hee Jung Choi, Trevor Chow, Aaron Wan, Hong Meng Yam, Swetha Yogeswaran, Beining Zhou
Deep Learning Model Transformer Based SemEval 2022 Task Sexist Content Online Sexism Explainable Detection GloVe Wearing Hand

May 6, 2023

May 4, 2023

May 2, 2023

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Chen Li, Yang Cao, Ye Zhu, Debo Cheng, Chengyuan Li, Yasuhiko Morimoto
Knowledge Graph Deep Learning Model Effective Recommendation Recommendation System Recommendation Policy

May 1, 2023

Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Yi Lin, Dong Zhang, Xiao Fang, Yufan Chen, Kwang-Ting Cheng, Hao Chen
Convolutional Neural Network Vision Transformer Deep Learning Model Medical Image Segmentation Learning Based Boundary Detection

April 29, 2023

X-ray Recognition: Patient identification from X-rays using a contrastive objective
Hao Liang, Kevin Ni, Guha Balakrishnan
Deep Learning Model Chest X Ray X Ray Patient Friendly Language Patient Data Contrastive Objective

April 28, 2023

April 25, 2023

April 21, 2023

Deep Learning Model

Papers

Model-based Programming: Redefining the Atomic Unit of Programming for the Deep Learning Era

Metric Space Magnitude and Generalisation in Neural Networks

SRIL: Selective Regularization for Class-Incremental Learning

Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data

TaLU: A Hybrid Activation Function Combining Tanh and Rectified Linear Unit to Enhance Neural Networks

Stanford MLab at SemEval-2023 Task 10: Exploring GloVe- and Transformer-Based Methods for the Explainable Detection of Online Sexism

Electromyography Signal Classification Using Deep Learning

Feature Chirality in Deep Learning Models

Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps

Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model

Neuralizer: General Neuroimage Analysis without Re-Training

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems

Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

X-ray Recognition: Patient identification from X-rays using a contrastive objective

Using a Deep Learning Model to Simulate Human Stock Trader's Methods of Chart Analysis

Synergy of Machine and Deep Learning Models for Multi-Painter Recognition

Optimizing Deep Learning Models For Raspberry Pi

Detection of Pavement Cracks by Deep Learning Models of Transformer and UNet

Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams

Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022