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

May 10, 2024

Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Training
Tareen Dawood, Bram Ruijsink, Reza Razavi, Andrew P. King, Esther Puyol-Antón
Deep Learning Medical Application Uncertainty Aware Training Deterministic Uncertainty

May 9, 2024

May 8, 2024

May 7, 2024

May 6, 2024

May 5, 2024

Confidential and Protected Disease Classifier using Fully Homomorphic Encryption
Aditya Malik, Nalini Ratha, Bharat Yalavarthi, Tilak Sharma, Arjun Kaushik, Charanjit Jutla
Deep Learning Homomorphic Encryption Conversational AI Diagnosis System

Deep Learning

Papers

Improving Deep Learning Model Calibration for Cardiac Applications using Deterministic Uncertainty Networks and Uncertainty-aware Training

Transforming the Bootstrap: Using Transformers to Compute Scattering Amplitudes in Planar N = 4 Super Yang-Mills Theory

Deep Learning-Based Residual Useful Lifetime Prediction for Assets with Uncertain Failure Modes

Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework

Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue

Real-Time Pill Identification for the Visually Impaired Using Deep Learning

Physics-based deep learning reveals rising heating demand heightens air pollution in Norwegian cities

Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification

Leveraging LSTM and GAN for Modern Malware Detection

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium

A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model

Philosophy of Cognitive Science in the Age of Deep Learning

Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep Learning Techniques

Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models

On Adversarial Examples for Text Classification by Perturbing Latent Representations

Federated Learning Privacy: Attacks, Defenses, Applications, and Policy Landscape - A Survey

Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up

Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series

Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

Confidential and Protected Disease Classifier using Fully Homomorphic Encryption