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
Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
Ziwen Kan, Shahbaz Rezaei, Xin Liu
ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back Propagation
Lujia Zhong, Shuo Huang, Yonggang Shi
Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)
Yansel Gonzalez Tejeda, Helmut A. Mayer
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features
Shaoxiang Dang, Tetsuya Matsumoto, Yoshinori Takeuchi, Takashi Tsuboi, Yasuhiro Tanaka, Daisuke Nakatsubo, Satoshi Maesawa, Ryuta Saito, Masahisa Katsuno, Hiroaki Kudo
Approaching Deep Learning through the Spectral Dynamics of Weights
David Yunis, Kumar Kshitij Patel, Samuel Wheeler, Pedro Savarese, Gal Vardi, Karen Livescu, Michael Maire, Matthew R. Walter
Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation
Vipin Singh, Tianheng Ling, Teodor Chiaburu, Felix Biessmann
Active learning for efficient data selection in radio-signal based positioning via deep learning
Vincent Corlay, Milan Courcoux-Caro
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks
Zhengjia Xu, Dingyang Lyu, Jinghui Zhang
Image Score: Learning and Evaluating Human Preferences for Mercari Search
Chingis Oinar, Miao Cao, Shanshan Fu
Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning
Zhengyi Lu, Hao Liang, Xiao Wang, Xinqiang Yan, Yuankai Huo
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer
Georgios Is. Detorakis
A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning
Deyu Li, Longfei Xiao, Handi Wei, Yan Li, Binghua Zhang
Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
Udo Schlegel, Daniel A. Keim, Tobias Sutter
BAUST Lipi: A BdSL Dataset with Deep Learning Based Bangla Sign Language Recognition
Md Hadiuzzaman, Mohammed Sowket Ali, Tamanna Sultana, Abdur Raj Shafi, Abu Saleh Musa Miah, Jungpil Shin
Demystifying the Communication Characteristics for Distributed Transformer Models
Quentin Anthony, Benjamin Michalowicz, Jacob Hatef, Lang Xu, Mustafa Abduljabbar, Aamir Shafi, Hari Subramoni, Dhabaleswar Panda
PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
Jason Matthews, Alex Bihlo