In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs
- URL: http://arxiv.org/abs/2406.01808v1
- Date: Mon, 3 Jun 2024 21:59:21 GMT
- Title: In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs
- Authors: Grzegorz Kaszuba, Amirhossein D. Naghdi, Dario Massa, Stefanos Papanikolaou, Andrzej Jaszkiewicz, Piotr Sankowski,
- Abstract summary: In-context learning allows for performing nontrivial machine learning tasks during inference only.
In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties?
We employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information.
- Score: 1.8635507597668244
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties? However, this would not be possible for structure property prediction tasks unless an effective method is found to pass atomic-level geometric features to the transformer model. To address this problem, we employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information. To demonstrate our model's capabilities, we partition the QM9 dataset into sequences of molecules that share a common substructure and use them for in-context learning. This approach significantly improves the performance of the model on out-of-distribution examples, surpassing the one of general graph neural network models.
Related papers
- Discovering interpretable elastoplasticity models via the neural
polynomial method enabled symbolic regressions [0.0]
Conventional neural network elastoplasticity models are often perceived as lacking interpretability.
This paper introduces a two-step machine learning approach that returns mathematical models interpretable by human experts.
arXiv Detail & Related papers (2023-07-24T22:22:32Z) - FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations.
We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
arXiv Detail & Related papers (2023-04-28T21:48:31Z) - Robust Graph Representation Learning via Predictive Coding [46.22695915912123]
Predictive coding is a message-passing framework initially developed to model information processing in the brain.
In this work, we build models that rely on the message-passing rule of predictive coding.
We show that the proposed models are comparable to standard ones in terms of performance in both inductive and transductive tasks.
arXiv Detail & Related papers (2022-12-09T03:58:22Z) - Towards a mathematical understanding of learning from few examples with
nonlinear feature maps [68.8204255655161]
We consider the problem of data classification where the training set consists of just a few data points.
We reveal key relationships between the geometry of an AI model's feature space, the structure of the underlying data distributions, and the model's generalisation capabilities.
arXiv Detail & Related papers (2022-11-07T14:52:58Z) - Generalization Properties of Retrieval-based Models [50.35325326050263]
Retrieval-based machine learning methods have enjoyed success on a wide range of problems.
Despite growing literature showcasing the promise of these models, the theoretical underpinning for such models remains underexplored.
We present a formal treatment of retrieval-based models to characterize their generalization ability.
arXiv Detail & Related papers (2022-10-06T00:33:01Z) - Curvature-informed multi-task learning for graph networks [56.155331323304]
State-of-the-art graph neural networks attempt to predict multiple properties simultaneously.
We investigate a potential explanation for this phenomenon: the curvature of each property's loss surface significantly varies, leading to inefficient learning.
arXiv Detail & Related papers (2022-08-02T18:18:41Z) - A Physics-Guided Neural Operator Learning Approach to Model Biological
Tissues from Digital Image Correlation Measurements [3.65211252467094]
We present a data-driven correlation to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios.
A material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve leaflet.
The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material properties learned implicitly from the data and naturally embedded in the network parameters.
arXiv Detail & Related papers (2022-04-01T04:56:41Z) - Towards Open-World Feature Extrapolation: An Inductive Graph Learning
Approach [80.8446673089281]
We propose a new learning paradigm with graph representation and learning.
Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
arXiv Detail & Related papers (2021-10-09T09:02:45Z) - Model-agnostic multi-objective approach for the evolutionary discovery
of mathematical models [55.41644538483948]
In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results.
We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
arXiv Detail & Related papers (2021-07-07T11:17:09Z) - Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine
Learning on Graphs with Applications to Modeling of Cytoskeleton [0.0]
We define a novel type of ensemble Graph Convolutional Network (GCN) model.
Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final prediction.
arXiv Detail & Related papers (2020-02-14T01:56:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.