WebApr 10, 2024 · The critical roles of computations and machine learning in accelerating materials discovery have become increasingly recognized, particularly in predicting and interpreting the synthesizability and functionality of new materials. Here, we develop a synthesizable materials discovery scheme using interpretable, physics-informed … WebWe make two contributions. First, we introduce a deep learning system that learns intuitive physics directly from visual data, using object-level representations inspired by studies of visual cognition in children. Second, we introduce and open-source a machine-learning dataset designed to evaluate conceptual understanding of intuitive physics ...
(PDF) Interpretable physics-based models compared with …
WebJan 26, 2024 · Now that we built a model, it’s time to get busy with interpretation tools that can explain the predictions of our model. We’ll start with one of the most popular tools for this, ELI5. 1. ELI5. ELI5 is an acronym for ‘Explain Like I’m 5’. It’s a Python library that’s popular because it’s easy to use. WebInspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different physical properties. In order to demonstrate that our system models these underlying physical properties, we train our model on collisions of different shapes (cube, cone, cylinder, spheres etc.) and test … blues big city adventure 2022
Explainable vs Interpretable AI: An Intuitive Example - Medium
WebAug 29, 2024 · Inspired by this observation, we propose an interpretable intuitive physics model where specific dimensions in the bottleneck layers correspond to different … WebJul 11, 2024 · Second, to build a model capable of learning intuitive physics, we endowed our model with object-centric representation and computation directly inspired by accounts of infant intuitive physics ... WebJul 11, 2024 · An explanation is created by approximating the underlying model locally by an interpretable one. Interpretable models are e.g. linear models with strong regularisation, decision tree’s, etc. The interpretable models are trained on small perturbations of the original instance and should only provide a good local approximation. blues bikes and bayous