Deep Model
Deep models, encompassing a broad range of neural network architectures, aim to learn complex patterns from data for various tasks like image classification, time series forecasting, and system identification. Current research emphasizes improving efficiency (e.g., through constant-time learning algorithms and layer caching), enhancing explainability (e.g., via gradient-free methods), and mitigating issues like bias and memorization. These advancements are significant because they improve the reliability, trustworthiness, and applicability of deep models across diverse scientific fields and real-world applications, including healthcare, finance, and autonomous systems.
Papers
Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos, Nicu Sebe, Jurandy Almeida
PAGER: A Framework for Failure Analysis of Deep Regression Models
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Puja Trivedi, Rushil Anirudh
Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Charlie Hou, Kiran Koshy Thekumparampil, Michael Shavlovsky, Giulia Fanti, Yesh Dattatreya, Sujay Sanghavi
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Baoquan Zhang, Chuyao Luo, Demin Yu, Huiwei Lin, Xutao Li, Yunming Ye, Bowen Zhang
Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference
Lei Li, Julia Camps, Zhinuo, Wang, Abhirup Banerjee, Marcel Beetz, Blanca Rodriguez, Vicente Grau
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
Haixing Dai, Lu Zhang, Lin Zhao, Zihao Wu, Zhengliang Liu, David Liu, Xiaowei Yu, Yanjun Lyu, Changying Li, Ninghao Liu, Tianming Liu, Dajiang Zhu