Search Results for author: Yuan Yin

Found 17 papers, 8 papers with code

Accurate stochastic simulation algorithm for multiscale models of infectious diseases

1 code implementation7 Jun 2024 Yuan Yin, Jennifer A. Flegg, Mark B. Flegg

In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically.

Proposal-based Temporal Action Localization with Point-level Supervision

no code implementations9 Oct 2023 Yuan Yin, Yifei HUANG, Ryosuke Furuta, Yoichi Sato

Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action instance is annotated in training data.

Action Classification Multiple Instance Learning +1

INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations

no code implementations25 Jul 2023 Louis Serrano, Leon Migus, Yuan Yin, Jocelyn Ahmed Mazari, Patrick Gallinari

For numerical design, the development of efficient and accurate surrogate models is paramount.

Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations

1 code implementation9 Jun 2023 Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue

We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data, such as irregular samples, missing data, or unaligned measurements from multiple sensors.

Imputation Meta-Learning +1

Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling

no code implementations18 Jan 2023 Carlo Metta, Riccardo Guidotti, Yuan Yin, Patrick Gallinari, Salvatore Rinzivillo

We propose a use case study, for skin lesion diagnosis, illustrating how it is possible to provide the practitioner with explanations on the decisions of a state of the art deep neural network classifier trained to characterize skin lesions from examples.

Continuous PDE Dynamics Forecasting with Implicit Neural Representations

1 code implementation29 Sep 2022 Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari

Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations.

Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators

1 code implementation29 Jun 2022 Léon Migus, Yuan Yin, Jocelyn Ahmed Mazari, Patrick Gallinari

In this work, we study three multi-resolution schema with integral kernel operators that can be approximated with \emph{Message Passing Graph Neural Networks} (MPGNNs).

Generalizing to New Physical Systems via Context-Informed Dynamics Model

1 code implementation1 Feb 2022 Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, Patrick Gallinari

Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different physical contexts.

Explainable Deep Image Classifiers for Skin Lesion Diagnosis

no code implementations22 Nov 2021 Carlo Metta, Andrea Beretta, Riccardo Guidotti, Yuan Yin, Patrick Gallinari, Salvatore Rinzivillo, Fosca Giannotti

A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems.

Decision Making Explainable artificial intelligence +2

Patent-KG: Patent Knowledge Graph Use for Engineering Design

no code implementations26 Aug 2021 Haoyu Zuo, Yuan Yin, Peter Childs

This paper builds a patent-based knowledge graph, patent-KG, to represent the knowledge facts in patents for engineering design.


LEADS: Learning Dynamical Systems that Generalize Across Environments

1 code implementation NeurIPS 2021 Yuan Yin, Ibrahim Ayed, Emmanuel de Bézenac, Nicolas Baskiotis, Patrick Gallinari

Both are sub-optimal: the former disregards the discrepancies between environments leading to biased solutions, while the latter does not exploit their potential commonalities and is prone to scarcity problems.

Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting

2 code implementations ICLR 2021 Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.

Unsupervised Spatiotemporal Data Inpainting

no code implementations25 Sep 2019 Yuan Yin, Arthur Pajot, Emmanuel de Bézenac, Patrick Gallinari

We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner.

Generative Adversarial Network

Scalable Graph Embeddings via Sparse Transpose Proximities

no code implementations16 May 2019 Yuan Yin, Zhewei Wei

Based on the concept of transpose proximity, we design \strap, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously.

Graph Embedding

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