Data-Efficient Learning

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14 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

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Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
May 7, 2026Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2Latent Dynamics Modeling Physics-Informed ML
Leibniz Universität Hannover · Saarland University · Uppsala University
SPADE: Faster Drug Discovery by Learning from Sparse Data
May 6, 2026Rahul Nandakumar, Ben Fauber, Deepayan ChakrabartiMolecular Optimization AI-Assisted Scientific Research
McCombs School of Business University of Texas at Austin Austin, TX · NVIDIA
RD-ViT: Recurrent-Depth Vision Transformer for Semantic Segmentation with Reduced Data Dependence Extending the Recurrent-Depth Transformer Architecture to Dense Prediction
May 5, 2026Renjie HeMedical Image Analysis Recurrent Transformers
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
May 3, 2026Murilo Batista, Shirin Salehi, Saeed Mashdour +3Representation Learning Embedding Compression
Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, Germany · Centre for Telecommunications Studies, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22541-041, Brazil
Personalized Digital Health Modeling with Adaptive Support Users
May 3, 2026Zhongqi Yang, Mahkameh Rasouli, Neda Mohseni +3Transfer Learning User Modeling
Department of Computer Science, University of California, Irvine · Sue & Bill Gross School of Nursing, University of California, Irvine · Thrive AI Health +1
GraphSculptor: Sculpting Pre-training Coreset for Graph Self-supervised Learning
May 2, 2026Chuang Liu, Zelin Yao, Xueqi Ma +4Graph Representation Learning Self-Supervised Pre-Training
Sangfor Technologies Inc. · Wuhan University · The University of Melbourne +2
Developing a Strong Pre-Trained Base Model for Plant Leaf Disease Classification
May 2, 2026David J. RichterPlant Disease Classification Transfer Learning
Department of Artiﬁcial Intelligence Convergence Graduate School, Chonnam National University
Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
Apr 28, 2026Yuting Yang, Gang Mei, Feng Chen +2Data-Efficient Learning
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China · School of Geological Engineering and Geomatics, Chang’an University, Xi’an, 710064, China
Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning
Apr 27, 2026Arielle Sanford, Andrew T. Kamen, Frederic T. Chong +1Reduced-Order Modeling Data-Efficient Learning
Department of Computer Science, University of Chicago, Chicago, IL 60637 · Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637 · Johns Hopkins Applied Physics Laboratory, Laurel, MD 20723
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Apr 27, 2026Yiran Huang, Lukas Thede, Massimiliano Mancini +2Large Vision-Language Models Structured Pruning
Technical University of Munich, Germany · Munich Center for Machine Learning, Germany. · Helmholtz Munich, Germany. +3
Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data
Apr 25, 2026Long Jing, Zhixiong Yang, Yajun Zhang +1Cross-Modal Representation Learning Human Activity Recognition
College of Computer Science, Northwest University, Xi’an, China · Xinjiang University, Urumqi, China
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Apr 24, 2026Zixuan Wang, Xingyu Dang, Jason D. Lee +1Long-Tail Learning Compositional Reasoning
1Princeton University · University of California, Berke · 3Tsinghua University
Rethinking Dataset Distillation: Hard Truths about Soft Labels
Apr 20, 2026Priyam Dey, Aditya Sahdev, Sunny Bhati +2Training Data Selection Dataset Distillation
Vision and AI Lab, Indian Institute of Science, Bangalore · University of Maryland, Baltimore County · IIT Hyderabad
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
Apr 19, 2026Paul M. ThompsonNeural Scaling Laws Data-Efficient Learning
Stevens Institute for Neuroimaging & Informatics · University of Southern California · Los Angeles, CA, USA
Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data
Apr 19, 2026Vedanta Thapar, Abhinav GuptaNonlinear System Identification Recurrent Neural Networks
The Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom · Center for Theoretical Physics, St. Stephen’s College, University of Delhi, Delhi 110007, India
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Apr 19, 2026Zhiyin Yu, Bo Zhang, Qibin Hou +3Self-Training for Language Models RL for Language Model Reasoning
Peking University · Shanghai Artificial Intelligence Laboratory · Nankai University +1
Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics
Apr 17, 2026Abhishek Sawaika, Durga Pritam Suggisetti, Udaya Parampalli +1Hybrid Quantum-Classical ML High-Energy Physics
qCLOUDS Lab, School of Computing and Information Systems, The University of Melbourne, Australia · Birla Institute of Technology and Science Pilani, Dubai, UAE
Structure as Computation: Developmental Generation of Minimal Neural Circuits
Apr 16, 2026Duan ZhouData-Efficient Learning
Independent Researcher
Chaotic CNN for Limited Data Image Classification
Apr 16, 2026Anusree M, Akhila Henry, Pramod P NairNeural Network Generalization Convolutional Neural Networks
1*Department of Mathematics, Amrita Vishwa Vidyapeetham, Amritapuri, India.
Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers
Apr 13, 2026Miit Daga, Swarna Priya RamuFine-Tuning Data Selection
School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
Zero-shot World Models Are Developmentally Efficient Learners
Apr 11, 2026Khai Loong Aw, Klemen Kotar, Wanhee Lee +6Zero-Shot Learning Computational Cognitive Modeling
Stanford University
Semi-supervised CAPP Transformer Learning via Pseudo-labeling
Feb 1, 2026Dennis Gross, Helge Spieker, Arnaud Gotlieb +3Transformer Pseudo-Labeling
1- Simula Research Laboratory, Oslo Norway · 2- National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory Greece
Predictive Inorganic Synthesis based on Machine Learning using Small Data sets: a case study of Hydrodynamic Diameter-controlled Cu Nanoparticles
Dec 18, 2025Brent Motmans, Digvijay Ghogare, Thijs G. I. van Wijk +6Materials Property Prediction Materials Science
Hasselt University, Institute for Materials Research (IUMAT), Quantum & Artificial inTelligence design Of Materials (QuATOMs), Martelarenlaan 42, B-3500 Hasselt, Belgium · Hasselt University, Institute for Materials Research (IUMAT), Hybrid Materials Design (HyMaD), Martelarenlaan 42, B-3500 Hasselt, Belgium · imec, IUMAT, Wetenschapspark 1, B-3590 Diepenbeek, Belgium +4
Is Generation Required for Data-Efficient Perception?
Dec 9, 2025Jack Brady, Bernhard Schölkopf, Thomas Kipf +2Visual Representation Learning Generative Modeling
Max Planck Institute for Intelligent Systems, Tübingen, Germany · Tübingen AI Center · ELLIS Institute, Tübingen +1
Geometry as a Missing Axis of Representation Quality: The Variational Geometric Information Bottleneck under Data Scarcity
Nov 4, 2025Ronald KatendeIntrinsic Dimensionality Information Bottleneck
Department of Mathematics, Kabale University, Katuna Road, Kikungiri Hill, Kabale, 317, Uganda
MetaCaDI: A Meta-Learning Framework for Causal Discovery from Multiple Environments with Unknown Interventions
Oct 25, 2025Hans Jarett Ong, Yoichi Chikahara, Tomoharu IwataMeta-Learning Causal Discovery
Nara Institute of Science and Technology, Nara, Japan · NTT Communication Science Laboratories, Kyoto, Japan
CHUCKLE -- When Humans Teach AI To Learn Emotions The Easy Way
Oct 10, 2025Ankush Pratap Singh, Houwei Cao, Yong LiuCurriculum Learning Emotion Recognition
New York Institute of Technology, Department of Computer Science, New York, United States · New York University, Electrical and Computer Engineering Department, New York, United States
Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers
Sep 27, 2025Seongsoo Heo, Dong-Wan ChoiVision Transformer Inverse Problems
Inha University
Aligning Inductive Bias for Data-Efficient Generalization in State Space Models
Sep 25, 2025Qiyu Chen, Guozhang ChenSequence Modeling Data-Efficient Learning
School of Computer Science, National Key Laboratory for Multimedia Information Processing, Peking University, China · School of Physics, Peking University, China
RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction
Aug 22, 2025Anahita Asadi, Leonid Popryho, Inna Partin-VaisbandNeural Surrogate Modeling Graph Neural Networks
Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607 USA

May 7, 2026, cs.LGJ/K move · Enter open · S save

Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations

Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström, Thomas B. Schön+1

Leibniz Universität Hannover · Saarland University · Uppsala University

Latent Dynamics Modeling Physics-Informed ML Gaussian Process Regression Data-Efficient Learning

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In this paper, we propose Lagrangian Gaussian Processes (LGPs) for probabilistic and data-efficient learning of dynamics via discrete forced Euler-Lagrange equations. Importantly, the geometric structure of the Lagrange-d'Alembert principle, which governs the motion of dynamical systems, is preserved by construction in the absence of external forces. This allows learning physically consistent models that overcome erroneous drift in the system's energy, thereby providing stable long-term predictions. At the core of our approach lie linear operators for Gaussian process conditioning, constructed from discrete forced Euler-Lagrange equations and variational discretization schemes. Thereby and unlike prior work, the method enables learning dynamics from discrete position snapshots, i.e., without access to a system's velocities or momenta. This is particularly relevant for a large class of practical scenarios where only position measurements are available, for instance, in motion capture or visual servoing applications. We demonstrate the data-efficiency and generalization capabilities of the LGPs in various synthetic and real-world case studies, including a real-world soft robot with hysteresis. The experimental results underscore that the LGPs learn physically consistent dynamics with uncertainty quantification solely from sparse positional data and enable stable long-term predictions.

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Latest papers 158