Publications related to Model-Agnostic Learning to Meta-Learn

Related publications (22)

Drone-Based Urban Traffic Monitoring and User Behavior Prediction

As urban populations continue to expand and vehicle density increases, the need for effective traffic monitoring and management becomes increasingly critical. Urban areas face significant challenges such as growing congestion, safety concerns, and parking ...

EPFL2025

High-resolution predictions of the coolant properties for the 3D PWR core with artificial neural networks based on CTF

Marianna Papadionysiou

PSI and North Carolina State University are developing a high-resolution multi-physics core solver for Pressurized Water Reactor (PWR) analysis in Cartesian geometry, using the neutron transport code nTRACER and two Machine Learning (ML) models providing t ...

2025

Learning by Teaching: Designing Teachable Agents to Support Children's Pronunciation Skill Learning

Pierre Dillenbourg, Chenyang Wang, Lucas Gérard Julien Burget

Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to addr ...

Association for Computing Machinery2025

On fundamental aspects of quantum extreme learning machines

Weijie Xiong, Mehrad Sahebi, Giorgio Facelli, Zoë Holmes, Thanasilp Supanut

Quantum extreme learning machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a quantum substrate-the quantum reservoir-and the efficient post-measurement ...

SPRINGERNATURE2025

Causality: fundamental principles and tools

Elise Hélène Dumas

This chapter aims to provide a gentle introduction to Causal Learning (CL), and motivation for its application to medical image analysis, seeking for more robustness against data and domain drifts, and a reliable tool to answer counterfactual questions and ...

Academic Press2025

Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild

Liangze Jiang

Neural architectures tend to fit their data with relatively simple functions. This "simplicity bias" is widely regarded as key to their success. This paper explores the limits of this principle. Building on recent findings that the simplicity bias stems fr ...

IEEE2025

Differences in Visual Perception in Humans and Deep Neural Networks

Ben Henrik Lönnqvist

Visual grouping is the ability to combine parts of objects to form whole objects, and it is crucial for general and robust vision. Despite this, a comprehensive model of how the process works in humans remains elusive. In my thesis, I investigate how well ...

EPFL2025

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce, Luca Arnaboldi

We study the impact of the batch

n_b

on the iteration time

T

of training two-layer neural networks with one-pass stochastic gradient descent (SGD) on multi-index target functions of isotropic covariates. We characterize the optimal batch size minimizin ...

JMLR2024

Hierarchical Training of Deep Neural Networks Using Early Exiting

Pascal Frossard, Liza Carol Andrea Dunbar, Ahmet Caner Yüzügüler, Yamin Sepehri

Deep neural networks (DNNs) provide state-of-the-art accuracy for vision tasks, but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication ...

Ieee-Inst Electrical Electronics Engineers Inc2024

∇2DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials

Egor Rumiantsev

Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. However, high computational complexity limits the scalability of their applica ...

Neural information processing systems foundation2024

Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning

Frédéric Courbin, Georges Meylan, Austin Chandler Peel

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samp ...

2024

Almost exact recovery in noisy semi-supervised learning

Maximilien Claude Robert Dreveton

2024.Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classification. In particular, we derive the maximum a posteriori (MAP) estima ...

2024

Arrows of Time for Large Language Models

Clément Hongler, Vassilis Joseph Papadopoulos

We study the probabilistic modeling performed by Autoregressive Large Language Models (LLMs) through the angle of time directionality, addressing a question first raised in (Shannon, 1951). For large enough models, we empirically find a time asymmetry in t ...

ACM2024

A Joint Graph Signal and Laplacian Denoising Network

Zepeng Zhang

Graph neural network (GNN) models have presented astonishing achievements in various application fields. However, they are shown to be vulnerable to adversarial attacks on graph structure and unnoticeable perturbations on the graph structure can cause sign ...

Institute of Electrical and Electronics Engineers Inc.2024

Programming nonlinear propagation for efficient optical learning machines

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ugur Tegin, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability. Optics is a promising contender for providing lower power com ...

Spie-Soc Photo-Optical Instrumentation Engineers2024

Stable Linear Subspace Identification: A Machine Learning Approach

Colin Neil Jones, Giancarlo Ferrari Trecate

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete ...

Institute of Electrical and Electronics Engineers Inc.2024

A Predictive Model for Tactile Force Estimation Using Audio-Tactile Data

Dominic Reber, Aude Billard, Ahalya Prabhakar, Loïc Niederhauser

Robust in-hand manipulation of objects with movable content requires estimation and prediction of the contents' motion with enough anticipation to allow time to compensate for resulting internal torques. The quick estimation of the objects' dynamics can be ...

Ieee-Inst Electrical Electronics Engineers Inc2024

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Thomas Tim Holvoet, Cécile Hébert, Duncan Thomas Lindsay Alexander, Nathanaël Perraudin, Guillaume Romain Obozinski

We present the development of a new algorithm which combines state-of-the-art energy-dispersive x-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spec ...

2024

Automated fiducial-based alignment of cryo-electron tomography tilt series in Dynamo

Henning Paul-Julius Stahlberg

With the advent of modern technologies for cryo-electron tomography (cryo-ET), high-quality tilt series are more rapidly acquired than processed and analyzed. Thus, a robust and fast-automated alignment for batch processing in cryo-ET is needed. While diff ...

CELL PRESS2024

AmadeusGPT: a natural language interface for interactive animal behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Jessy Lauer, Mu Zhou

The process of quantifying and analyzing animal behavior involves translating the naturally occurring descriptive language of their actions into machine-readable code. Yet, codifying behavior analysis is often challenging without deep understanding of anim ...

2023