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

October 24, 2023

EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification
Benyamin Haghi, Lin Ma, Sahin Lale, Anima Anandkumar, Azita Emami
Deep Learning Analog Neural Network Analog Computing Virtual Analog Arrhythmia Classification

October 23, 2023

October 21, 2023

October 20, 2023

October 19, 2023

Deep Learning

Papers

EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification

Serverless Federated Learning with flwr-serverless

Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation

Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias

The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management

Data Augmentation: a Combined Inductive-Deductive Approach featuring Answer Set Programming

Guidance system for Visually Impaired Persons using Deep Learning and Optical flow

TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images

Unleashing Modified Deep Learning Models in Efficient COVID19 Detection

Automatic Pronunciation Assessment -- A Review

Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic Fiber Composites

RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning

Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space

Application of deep learning for livestock behaviour recognition: A systematic literature review

SigFormer: Signature Transformers for Deep Hedging

Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches

Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

To grok or not to grok: Disentangling generalization and memorization on corrupted algorithmic datasets

Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey

Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning