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 from Contrastive Prompts: Automated Optimization and Adaptation
Mingqi Li, Karan Aggarwal, Yong Xie, Aitzaz Ahmad, Stephen Lau
CushionCatch: Compliant Catching Mechanism for Mobile Manipulators via Combined Optimization and Learning
Bingjie Chen, Keyu Fan, Houde Liu, Chongkun Xia, Liang Han, Bin Liang
Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation
Taekyung Kim, Robin Inho Kee, Dimitra Panagou
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment
Yuxiao Chen, Kai Li, Wentao Bao, Deep Patel, Yu Kong, Martin Renqiang Min, Dimitris N. Metaxas
Predicting Coronary Heart Disease Using a Suite of Machine Learning Models
Jamal Al-Karaki, Philip Ilono, Sanchit Baweja, Jalal Naghiyev, Raja Singh Yadav, Muhammad Al-Zafar Khan
A Feature Generator for Few-Shot Learning
Heethanjan Kanagalingam, Thenukan Pathmanathan, Navaneethan Ketheeswaran, Mokeeshan Vathanakumar, Mohamed Afham, Ranga Rodrigo
Learning to Play Video Games with Intuitive Physics Priors
Abhishek Jaiswal, Nisheeth Srivastava
Learning to Simulate Aerosol Dynamics with Graph Neural Networks
Fabiana Ferracina, Payton Beeler, Mahantesh Halappanavar, Bala Krishnamoorthy, Marco Minutoli, Laura Fierce
Stimulus-to-Stimulus Learning in RNNs with Cortical Inductive Biases
Pantelis Vafidis, Antonio Rangel
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
Yuxuan Hu, Chenwei Zhang, Min Yang, Xiaodan Liang, Chengming Li, Xiping Hu
Learning to Compare Hardware Designs for High-Level Synthesis
Yunsheng Bai, Atefeh Sohrabizadeh, Zijian Ding, Rongjian Liang, Weikai Li, Ding Wang, Haoxing Ren, Yizhou Sun, Jason Cong
Across-Game Engagement Modelling via Few-Shot Learning
Kosmas Pinitas, Konstantinos Makantasis, Georgios N. Yannakakis
Learning Multi-Manifold Embedding for Out-Of-Distribution Detection
Jeng-Lin Li, Ming-Ching Chang, Wei-Chao Chen
AutoMode-ASR: Learning to Select ASR Systems for Better Quality and Cost
Ahmet Gündüz, Yunsu Kim, Kamer Ali Yuksel, Mohamed Al-Badrashiny, Thiago Castro Ferreira, Hassan Sawaf
Learning to Coordinate without Communication under Incomplete Information
Shenghui Chen, Shufang Zhu, Giuseppe De Giacomo, Ufuk Topcu
Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
Ruiyang Hong, Anastasis Kratsios
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL
Hideo Kobayashi, Wuwei Lan, Peng Shi, Shuaichen Chang, Jiang Guo, Henghui Zhu, Zhiguo Wang, Patrick Ng
A Unified Framework for Neural Computation and Learning Over Time
Stefano Melacci, Alessandro Betti, Michele Casoni, Tommaso Guidi, Matteo Tiezzi, Marco Gori
Learning Spatially-Aware Language and Audio Embedding
Bhavika Devnani, Skyler Seto, Zakaria Aldeneh, Alessandro Toso, Elena Menyaylenko, Barry-John Theobald, Jonathan Sheaffer, Miguel Sarabia
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation
Jan Achterhold, Suresh Guttikonda, Jens U. Kreber, Haolong Li, Joerg Stueckler