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

August 15, 2023

August 14, 2023

August 13, 2023

August 12, 2023

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson, Christoph Gerhard Lisson, Meinrad Beer, Michael Götz, Timo Ropinski
Deep Learning Large Scale Medical Imaging Masked Autoencoders Self Supervised Pre Training Small Datasets Medical Image Classification Task Self Supervised Pre Training Method

August 11, 2023

Parametric Leaky Tanh: A New Hybrid Activation Function for Deep Learning
Stamatis Mastromichalakis
Deep Learning Deep Neural Network Activation Function

August 10, 2023

August 9, 2023

Deep Learning

Papers

The Challenge of Fetal Cardiac MRI Reconstruction Using Deep Learning

Learning to Identify Critical States for Reinforcement Learning from Videos

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System

The Performance of Transferability Metrics does not Translate to Medical Tasks

Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides

Diving with Penguins: Detecting Penguins and their Prey in Animal-borne Underwater Videos via Deep Learning

Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data

SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection

Deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks

How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation

Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models

StairNetV3: Depth-aware Stair Modeling using Deep Learning

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging

Parametric Leaky Tanh: A New Hybrid Activation Function for Deep Learning

Exploring Deep Learning Approaches to Predict Person and Vehicle Trips: An Analysis of NHTS Data

Reviewing 3D Object Detectors in the Context of High-Resolution 3+1D Radar

Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels

A Novel Method for improving accuracy in neural network by reinstating traditional back propagation technique

Optimizing a Transformer-based network for a deep learning seismic processing workflow

Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores