LeArning Abstract
Learning, in the context of these papers, encompasses a broad range of research focused on improving the efficiency, robustness, and adaptability of machine learning models across diverse applications. Current efforts concentrate on developing novel self-supervised learning techniques, particularly for structured data like tabular formats, and on leveraging low-rank adaptations for efficient fine-tuning of large language and other foundation models. These advancements are significant because they address key challenges in data efficiency, computational cost, and the generalization capabilities of machine learning systems, impacting fields ranging from personalized medicine to autonomous robotics.
Papers
Learning to rank quantum circuits for hardware-optimized performance enhancement
Gavin S. Hartnett, Aaron Barbosa, Pranav S. Mundada, Michael Hush, Michael J. Biercuk, Yuval Baum
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
Beomseok Kang, Harshit Kumar, Minah Lee, Biswadeep Chakraborty, Saibal Mukhopadhyay
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana
Using Few-Shot Learning to Classify Primary Lung Cancer and Other Malignancy with Lung Metastasis in Cytological Imaging via Endobronchial Ultrasound Procedures
Ching-Kai Lin, Di-Chun Wei, Yun-Chien Cheng
Learning 3D-Aware GANs from Unposed Images with Template Feature Field
Xinya Chen, Hanlei Guo, Yanrui Bin, Shangzhan Zhang, Yuanbo Yang, Yue Wang, Yujun Shen, Yiyi Liao
Learning a Category-level Object Pose Estimator without Pose Annotations
Fengrui Tian, Yaoyao Liu, Adam Kortylewski, Yueqi Duan, Shaoyi Du, Alan Yuille, Angtian Wang
Factored Task and Motion Planning with Combined Optimization, Sampling and Learning
Joaquim Ortiz-Haro
Learning From Simplicial Data Based on Random Walks and 1D Convolutions
Florian Frantzen, Michael T. Schaub
Learning to Plan and Generate Text with Citations
Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan, Mirella Lapata
Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods
Vishnunandan L. N. Venkatesh, Byung-Cheol Min
Is Meta-training Really Necessary for Molecular Few-Shot Learning ?
Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed
Automatic Derivation of an Optimal Task Frame for Learning and Controlling Contact-Rich Tasks
Ali Mousavi Mohammadi, Maxim Vochten, Erwin Aertbeliën, Joris De Schutter
Contextual Embedding Learning to Enhance 2D Networks for Volumetric Image Segmentation
Zhuoyuan Wang, Dong Sun, Xiangyun Zeng, Ruodai Wu, Yi Wang
Learning to Control Camera Exposure via Reinforcement Learning
Kyunghyun Lee, Ukcheol Shin, Byeong-Uk Lee