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
Knowledge Distillation for Anomaly Detection
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos, Nadezda Chernyavskaya, Philip Harris, Maurizio Pierini, Isobel Ojalvo, Peter Elmer
Large-Scale OD Matrix Estimation with A Deep Learning Method
Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng
Hierarchical Reinforcement Learning for Temporal Pattern Prediction
Faith Johnson, Kristin Dana
Learning to Predict Structural Vibrations
Jan van Delden, Julius Schultz, Christopher Blech, Sabine C. Langer, Timo Lüddecke
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification
Anil B. Gavade, Neel Kanwal, Priyanka A. Gavade, Rajendra Nerli
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis
Glejdis Shkëmbi, Johanna P. Müller, Zhe Li, Katharina Breininger, Peter Schüffler, Bernhard Kainz
Euclid: Identification of asteroid streaks in simulated images using deep learning
M. Pöntinen, M. Granvik, A. A. Nucita, L. Conversi, B. Altieri, B. Carry, C. M. O'Riordan, D. Scott, N. Aghanim, A. Amara, L. Amendola, N. Auricchio, M. Baldi, D. Bonino, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, J. Dinis, F. Dubath, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kümmel, S. Kermiche, A. Kiessling, T. Kitching, R. Kohley, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, E. Medinaceli, S. Mei, M. Melchior, Y. Mellier, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S. -M. Niemi, T. Nutma, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, S. Serrano, C. Sirignano, G. Sirri, L. Stanco, P. Tallada-Crespí, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, T. Vassallo, G. Verdoes Kleijn, Y. Wang, J. Weller, G. Zamorani, J. Zoubian, V. Scottez
Grokking as a First Order Phase Transition in Two Layer Networks
Noa Rubin, Inbar Seroussi, Zohar Ringel
Visual inspection for illicit items in X-ray images using Deep Learning
Ioannis Mademlis, Georgios Batsis, Adamantia Anna Rebolledo Chrysochoou, Georgios Th. Papadopoulos
A Quantitatively Interpretable Model for Alzheimer's Disease Prediction Using Deep Counterfactuals
Kwanseok Oh, Da-Woon Heo, Ahmad Wisnu Mulyadi, Wonsik Jung, Eunsong Kang, Kun Ho Lee, Heung-Il Suk
Domain Generalization for Medical Image Analysis: A Survey
Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski, Heung-Il Suk
PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches
Zherui Li, Raye Chen-Hua Yeow
Enhancing Accuracy in Deep Learning Using Random Matrix Theory
Leonid Berlyand, Etienne Sandier, Yitzchak Shmalo, Lei Zhang
Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data
Son Nguyen, Adam Wang, Albert Montillo
Something for (almost) nothing: Improving deep ensemble calibration using unlabeled data
Konstantinos Pitas, Julyan Arbel
Incorporating Target Vehicle Trajectories Predicted by Deep Learning Into Model Predictive Controlled Vehicles
Ni Dang, Zengjie Zhang, Jizheng Liu, Marion Leibold, Martin Buss
Enhancing Energy-efficiency by Solving the Throughput Bottleneck of LSTM Cells for Embedded FPGAs
Chao Qian, Tianheng Ling, Gregor Schiele