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

November 18, 2024

Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging
Zuzanna Buchnajzer, Kacper Dobek, Stanisław Hapke, Daniel Jankowski, Krzysztof Krawiec
Deep Learning Convolutional Neural Network Classification Code Medical Imaging Structured Representation Associative Memory Parallel Neurosymbolic
Artificial Scientific Discovery
Antonio Norelli
Deep Learning Line by Line Explanation Multimodal Model AI Researcher Exploratory Learning Autonomous Discovery

November 17, 2024

Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies
Kealan Dunnett, Reza Arablouei, Dimity Miller, Volkan Dedeoglu, Raja Jurdak
Deep Learning Timely Survey Adversarial Attack Deep Learning Model Global Evaluation Backdoor Attack Image Recognition
D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification
Minhee Jang, Juheon Son, Thanaporn Viriyasaranon, Junho Kim, Jang-Hwan Choi
Deep Learning Diffusion Model Classification Code Deep Learning Technology Model Based Diagnosis Face Connected Cube Hyper Representation

November 16, 2024

Deep Feature Response Discriminative Calibration
Wenxiang Xu, Tian Qiu, Linyun Zhou, Zunlei Feng, Mingli Song, Huiqiong Wang
Deep Learning Deep Neural Network Supervised ImageNet Discriminative Learning Feature Relevance Calibration Network
ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models
JooHyoung Cha, Munyoung Lee, Jinse Kwon, Jubin Lee, Jemin Lee, Yongin Kwon
Deep Learning Deep Learning Model Full Model

November 15, 2024

Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization
Fatahlla Moreh (Christian Albrechts University, Kiel, Germany), Yusuf Hasan (Aligarh Muslim University, Aligarh, India), Bilal Zahid Hussain (Texas A&M University, College Station, USA), Mohammad Ammar (Aligarh Muslim University, Aligarh, India), Sven Tomforde (Christian Albrechts University, Kiel, Germany)
Deep Learning Imbalanced Datasets Crack Detection Keypoint Localization
Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
Deep Learning Multi Task Learning Timely Communication Low Latency Machine Learning Task Task Oriented Communication Channel Modeling Multi Round
Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques
Maliheh Alaeddini
Deep Learning Machine Learning Comparative Study Underlying Emotion Emotion Detection Social Medium Site Reddit Text Based Emotion Detection
FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy
Rishit Kapoor (1), Jesher Joshua (2), Muralidharan Vijayarangan (3), Natarajan B (4) ((1) Vellore Institute of Technology, (2) Vellore Institute of Technology, (3) Vellore Institute of Technology, (4) Vellore Institute of Technology)
Deep Learning Supervised ImageNet Magnetic Resonance Imaging Medical Image Data Healthcare Data Federated Continual Learning Sensitive Patient Privacy Enhancing Technology Federated Ensemble
Towards Utilising a Range of Neural Activations for Comprehending Representational Associations
Laura O'Mahony, Nikola S. Nikolov, David JP O'Sullivan
Deep Learning Human Understanding Intermediate Representation Neuron Activation Range Query Output Activation Neural Network Activation
DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models
Atsushi Kudo
Deep Learning Numerical Weather Prediction Forecast Utterance
Building 6G Radio Foundation Models with Transformer Architectures
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Hatem Abou-Zeid
Deep Learning Vision Transformer Transformer Architecture G Wireless

November 14, 2024

Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Dimitrios Karslidis, Hatem Abou-Zeid
Deep Learning Self Supervised Learning Foundational Model Convolutional LSTM Radio Data
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
David W. Romero
Deep Learning High Efficiency Inductive Bias Greater Public Use Convolutional Operation Symmetry Breaking Continuous Model
Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical Cord
Marina A. Ayad, Ramin Nateghi, Abhishek Sharma, Lawrence Chillrud, Tilly Seesillapachai, Lee A.D. Cooper, Jeffery A. Goldstein
Deep Learning Digital Pathology Placenta Segmentation Fetal Health Stiff Metal Wire
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
Deep Learning Retrieval Augmented Generation Information Retrieval Open Source Large Language Model Biodiversity Monitoring Learning Approach

Deep Learning

Papers

Autoassociative Learning of Structural Representations for Modeling and Classification in Medical Imaging

Artificial Scientific Discovery

Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies

D-Cube: Exploiting Hyper-Features of Diffusion Model for Robust Medical Classification

Deep Feature Response Discriminative Calibration

ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models

Deep Learning for Micro-Scale Crack Detection on Imbalanced Datasets Using Key Point Localization

Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning

Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques

FedCL-Ensemble Learning: A Framework of Federated Continual Learning with Ensemble Transfer Learning Enhanced for Alzheimer's MRI Classifications while Preserving Privacy

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations

DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models

Building 6G Radio Foundation Models with Transformer Architectures

Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases

Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical Cord

Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications

The geometry of the deep linear network

Virtual teaching assistant for undergraduate students using natural language processing & deep learning

DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods