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Found 10 papers, 5 papers with code
Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling
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
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
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
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
A key issue in critical contexts such as medical diagnosis is the interpretability of the deep learning models adopted in decision-making systems.
Patent-KG: Patent Knowledge Graph Use for Engineering Design
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
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
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
We tackle the problem of inpainting occluded area in spatiotemporal sequences, such as cloud occluded satellite observations, in an unsupervised manner.
Scalable Graph Embeddings via Sparse Transpose Proximities
Based on the concept of transpose proximity, we design \strap, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously.
