Publications related to Learning to Assemble with Alternative Plans

Related publications (24)

Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

IOP Publishing Ltd2025

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

Auke Ijspeert, Astha Gupta

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

IOP Publishing Ltd2025

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Volkan Cevher, Fanghui Liu, Zhenyu Zhu

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

2025

Improving the accuracy and interpretability of neural networks for wind power forecasting

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

2025

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Christel Marie Tartini-Chappuis

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

EPFL2025

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Zaffar Ahmed Shaikh

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

SAGE PUBLICATIONS LTD2025

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Yves Perriard, Yoan René Cyrille Civet, Stefania Maria Aliki Konstantinidi, Simon Holzer, Andres Osorio Salazar, Marc Michel Charles Favier

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

SPIE2025

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Zeki Doruk Erden

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

EPFL2025

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Neha Tarigopula

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Training hybrid neural networks with multimode optical nonlinearities using digital twins

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

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Walter de Gruyter GmbH2025

Domain shift, domain adaptation, and generalization: A focus on MRI

Tobias Kober, Meritxell Bach Cuadra, Veronica Lily Ravano, Jonas Richiardi

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Academic Press2025

Accurate neural quantum states for interacting lattice bosons

Giuseppe Carleo, Zakari Denis

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacti ...

Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften2025

Context selectivity with dynamic availability enables lifelong continual learning

Wulfram Gerstner, Guillaume Emmanuel Fernand Bellec, Martin Louis Lucien Rémy Barry

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Elsevier BV2025

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Emanuela De Falco

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

2025

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui, Freya Behrens

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

2025

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Amaury Pierre Jiezhi Wei, Olga Fink

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

NATURE PORTFOLIO2025

Probing the latent hierarchical structure of data via diffusion models

Noam Itzhak Levi, Alessandro Favero, Antonio Sclocchi

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

2025

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Amir Jafargholi

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

Royal Society of Chemistry (RSC)2025

Multi-robot collaborative manufacturing driven by digital twins: Advancements, challenges, and future directions

Sichao Liu

Multi-robot systems envisioned for future factories will promote advancements and capabilities of handling complex tasks and realising optimal robotic operations. However, existing multi-robot systems face challenges such as integration complexity, difficu ...

2025

Advances on Real Time M/EEG Neural Feature Extraction

Dimitri Nestor Alice Van De Ville, Nicolas Henchoz, Delphine Ribes Lemay, Andreas Sonderegger, Emily Clare Groves, Patrick Karl Alois Neff, Lara Jacqueline Défayes, Danpeng Cai

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

IEEE2025