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
Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
Sheikh Junaid Fayaz, Nestor Montiel-Bohorquez, Shashank Bishnoi, Matteo Romano, Manuele Gatti, N. M. Anoop Krishnan
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring
Kamaljyoti Nath, Varun Kumar, Daniel J. Smith, George Em Karniadakis
Learning Human-Aware Robot Policies for Adaptive Assistance
Jason Qin, Shikun Ban, Wentao Zhu, Yizhou Wang, Dimitris Samaras
SPGL: Enhancing Session-based Recommendation with Single Positive Graph Learning
Tiantian Liang, Zhe Yang
Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning
RunLin Yu, Yipu Gong, Wenrui Li, Aiwen Sun, Mengren Zheng
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer Learning
Eloy Geenjaar, Lie Lu
Combating Semantic Contamination in Learning with Label Noise
Wenxiao Fan, Kan Li
Multilabel Classification for Lung Disease Detection: Integrating Deep Learning and Natural Language Processing
Maria Efimovich, Jayden Lim, Vedant Mehta, Ethan Poon
TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
Gangqiang Hu, Jianfeng Lu, Jianmin Han, Shuqin Cao, Jing Liu, Hao Fu
Paid with Models: Optimal Contract Design for Collaborative Machine Learning
Bingchen Wang, Zhaoxuan Wu, Fusheng Liu, Bryan Kian Hsiang Low
Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learning
Yun Qu, Yuhang Jiang, Boyuan Wang, Yixiu Mao, Cheems Wang, Chang Liu, Xiangyang Ji
On Distilling the Displacement Knowledge for Few-Shot Class-Incremental Learning
Pengfei Fang, Yongchun Qin, Hui Xue
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
Afsaneh Mahmoudi, Emil Björnson
Learning Semantic-Aware Representation in Visual-Language Models for Multi-Label Recognition with Partial Labels
Haoxian Ruan, Zhihua Xu, Zhijing Yang, Yongyi Lu, Jinghui Qin, Tianshui Chen
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
Hanwei Fan, Ya Wang, Sicheng Li, Tingyuan Liang, Wei Zhang
No Free Lunch for Defending Against Prefilling Attack by In-Context Learning
Zhiyu Xue, Guangliang Liu, Bocheng Chen, Kristen Marie Johnson, Ramtin Pedarsani
Interlocking-free Selective Rationalization Through Genetic-based Learning
Federico Ruggeri, Gaetano Signorelli
Controlling dynamical systems into unseen target states using machine learning
Daniel Köglmayr, Alexander Haluszczynski, Christoph Räth
Label-template based Few-Shot Text Classification with Contrastive Learning
Guanghua Hou, Shuhui Cao, Deqiang Ouyang, Ning Wang
Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning
Yi Gu, Zhaorui Wang, Dongjun Ye, Renjing Xu