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 Compress Contexts for Efficient Knowledge-based Visual Question Answering
Weixi Weng, Jieming Zhu, Hao Zhang, Xiaojun Meng, Rui Zhang, Chun Yuan
Cross-Refine: Improving Natural Language Explanation Generation by Learning in Tandem
Qianli Wang, Tatiana Anikina, Nils Feldhus, Simon Ostermann, Sebastian Möller, Vera Schmitt
Few-Shot Learning: Expanding ID Cards Presentation Attack Detection to Unknown ID Countries
Alvaro S. Rocamora, Juan M. Espin, Juan E. Tapia
Learning local and semi-local density functionals from exact exchange-correlation potentials and energies
Bikash Kanungo, Jeffrey Hatch, Paul M. Zimmerman, Vikram Gavini
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural Networks
Runzhong Wang, Yang Li, Junchi Yan, Xiaokang Yang
Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs
Aliakbar Nafar, Kristen Brent Venable, Parisa Kordjamshidi
Learning to Learn Transferable Generative Attack for Person Re-Identification
Yuan Bian, Min Liu, Xueping Wang, Yunfeng Ma, Yaonan Wang
Wind turbine condition monitoring based on intra- and inter-farm federated learning
Albin Grataloup, Stefan Jonas, Angela Meyer
From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents
Jifan Yu, Zheyuan Zhang, Daniel Zhang-li, Shangqing Tu, Zhanxin Hao, Rui Miao Li, Haoxuan Li, Yuanchun Wang, Hanming Li, Linlu Gong, Jie Cao, Jiayin Lin, Jinchang Zhou, Fei Qin, Haohua Wang, Jianxiao Jiang, Lijun Deng, Yisi Zhan, Chaojun Xiao, Xusheng Dai, Xuan Yan, Nianyi Lin, Nan Zhang, Ruixin Ni, Yang Dang, Lei Hou, Yu Zhang, Xu Han, Manli Li, Juanzi Li, Zhiyuan Liu, Huiqin Liu, Maosong Sun
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis
Xianbing Zhao, Lizhen Qu, Tao Feng, Jianfei Cai, Buzhou Tang
Granular-ball Representation Learning for Deep CNN on Learning with Label Noise
Dawei Dai, Hao Zhu, Shuyin Xia, Guoyin Wang
Oops, I Sampled it Again: Reinterpreting Confidence Intervals in Few-Shot Learning
Raphael Lafargue, Luke Smith, Franck Vermet, Mathias Löwe, Ian Reid, Vincent Gripon, Jack Valmadre
Exploring Sentiment Dynamics and Predictive Behaviors in Cryptocurrency Discussions by Few-Shot Learning with Large Language Models
Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova, Grigori Sidorov
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation
Tal Vol, Loai Danial, Nir Shlezinger
AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
Jinfeng Xu, Zheyu Chen, Jinze Li, Shuo Yang, Hewei Wang, Edith C.-H. Ngai