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
Accelerating Neural Network Training: A Brief Review
Sahil Nokhwal, Priyanka Chilakalapudi, Preeti Donekal, Suman Nokhwal, Saurabh Pahune, Ankit Chaudhary
Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark
Hassan Ismail Fawaz, Ganesh Del Grosso, Tanguy Kerdoncuff, Aurelie Boisbunon, Illyyne Saffar
Fragility, Robustness and Antifragility in Deep Learning
Chandresh Pravin, Ivan Martino, Giuseppe Nicosia, Varun Ojha
Physics-Informed Deep Learning of Rate-and-State Fault Friction
Cody Rucker, Brittany A. Erickson
A Cyber-Physical Architecture for Microgrids based on Deep learning and LORA Technology
Mojtaba Mohammadi, Abdollah KavousiFard, Mortza Dabbaghjamanesh, Mostafa Shaaban, Hatem. H. Zeineldin, Ehab Fahmy El-Saadany
Deep Learning with Physics Priors as Generalized Regularizers
Frank Liu, Agniva Chowdhury
PhyOT: Physics-informed object tracking in surveillance cameras
Kawisorn Kamtue, Jose M. F. Moura, Orathai Sangpetch, Paulo Garcia
Contractive error feedback for gradient compression
Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava, Georgios B. Giannakis
Vision Transformer-Based Deep Learning for Histologic Classification of Endometrial Cancer
Manu Goyal, Laura J. Tafe, James X. Feng, Kristen E. Muller, Liesbeth Hondelink, Jessica L. Bentz, Saeed Hassanpour
Kimad: Adaptive Gradient Compression with Bandwidth Awareness
Jihao Xin, Ivan Ilin, Shunkang Zhang, Marco Canini, Peter Richtárik
Plant Disease Recognition Datasets in the Age of Deep Learning: Challenges and Opportunities
Mingle Xu, Ji Eun Park, Jaehwan Lee, Jucheng Yang, Sook Yoon
Morphological Profiling for Drug Discovery in the Era of Deep Learning
Qiaosi Tang, Ranjala Ratnayake, Gustavo Seabra, Zhe Jiang, Ruogu Fang, Lina Cui, Yousong Ding, Tamer Kahveci, Jiang Bian, Chenglong Li, Hendrik Luesch, Yanjun Li
On the Dynamics Under the Unhinged Loss and Beyond
Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji
Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagery
Zelin Xu, Tingsong Xiao, Wenchong He, Yu Wang, Zhe Jiang
Deep Internal Learning: Deep Learning from a Single Input
Tom Tirer, Raja Giryes, Se Young Chun, Yonina C. Eldar
Deep Learning-based Sentiment Classification: A Comparative Survey
Mohamed Kayed, Rebeca P. Díaz-Redondo, Alhassan Mabrouk
Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu
Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning
Jiawei Sun, Bin Zhao, Dong Wang, Zhigang Wang, Jie Zhang, Nektarios Koukourakis, Juergen W. Czarske, Xuelong Li
When Bio-Inspired Computing meets Deep Learning: Low-Latency, Accurate, & Energy-Efficient Spiking Neural Networks from Artificial Neural Networks
Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter A. Beerel