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
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Birgit Kühbacher, Fernando Iglesias-Suarez, Niki Kilbertus, Veronika Eyring
Memorization in deep learning: A survey
Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang, Ming Ding, Chao Chen, Kok-Leong Ong, Jun Zhang, Yang Xiang
Improving the Fairness of Deep-Learning, Short-term Crime Prediction with Under-reporting-aware Models
Jiahui Wu, Vanessa Frias-Martinez
Robust Communication and Computation using Deep Learning via Joint Uncertainty Injection
Robert-Jeron Reifert, Hayssam Dahrouj, Alaa Alameer Ahmad, Haris Gacanin, Aydin Sezgin
Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers
Federico Bassetti, Marco Gherardi, Alessandro Ingrosso, Mauro Pastore, Pietro Rotondo
Interactive Image Selection and Training for Brain Tumor Segmentation Network
Matheus A. Cerqueira, Flávia Sprenger, Bernardo C. A. Teixeira, Alexandre X. Falcão
Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI
Jonghun Kim, Hyunjin Park
Tabular and Deep Learning for the Whittle Index
Francisco Robledo Relaño, Vivek Borkar, Urtzi Ayesta, Konstantin Avrachenkov
PETRA: Parallel End-to-end Training with Reversible Architectures
Stéphane Rivaud, Louis Fournier, Thomas Pumir, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
Guy Amir, Osher Maayan, Tom Zelazny, Guy Katz, Michael Schapira
Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising
Chang Zhou, Yang Zhao, Yuelin Zou, Jin Cao, Wenhan Fan, Yi Zhao, Chiyu Cheng
Fruit Classification System with Deep Learning and Neural Architecture Search
Christine Dewi, Dhananjay Thiruvady, Nayyar Zaidi
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning
Wanyu Bian
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks
Stefano Sarao Mannelli, Yaraslau Ivashynka, Andrew Saxe, Luca Saglietti
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
An expert-driven data generation pipeline for histological images
Roberto Basla, Loris Giulivi, Luca Magri, Giacomo Boracchi
Distributional Refinement Network: Distributional Forecasting via Deep Learning
Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong