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
Barttender: An approachable & interpretable way to compare medical imaging and non-imaging data
Ayush Singla, Shakson Isaac, Chirag J. Patel
Deep Learning-Driven Heat Map Analysis for Evaluating thickness of Wounded Skin Layers
Devakumar GR, JB Kaarthikeyan, Dominic Immanuel T, Sheena Christabel Pravin
DLBacktrace: A Model Agnostic Explainability for any Deep Learning Models
Vinay Kumar Sankarapu, Chintan Chitroda, Yashwardhan Rathore, Neeraj Kumar Singh, Pratinav Seth
Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation
Weiwen Ji, Jin Feng, Yueqi Liu, Yulu Qiu, Hua Gao
The Hermeneutic Turn of AI: Is the Machine Capable of Interpreting?
Remy Demichelis
Motif Channel Opened in a White-Box: Stereo Matching via Motif Correlation Graph
Ziyang Chen, Yongjun Zhang, Wenting Li, Bingshu Wang, Yong Zhao, C. L. Philip Chen
Countering Backdoor Attacks in Image Recognition: A Survey and Evaluation of Mitigation Strategies
Kealan Dunnett, Reza Arablouei, Dimity Miller, Volkan Dedeoglu, Raja Jurdak
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 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)
Low-Latency Task-Oriented Communications with Multi-Round, Multi-Task Deep Learning
Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques
Maliheh Alaeddini
Towards Utilising a Range of Neural Activations for Comprehending Representational Associations
Laura O'Mahony, Nikola S. Nikolov, David JP O'Sullivan
DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models
Atsushi Kudo
Building 6G Radio Foundation Models with Transformer Architectures
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Hatem Abou-Zeid
Self-Supervised Radio Pre-training: Toward Foundational Models for Spectrogram Learning
Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Dimitrios Karslidis, Hatem Abou-Zeid
The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases
David W. Romero
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