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Found 9 papers, 2 papers with code
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
To reduce the need for training data with simulated solutions, we pretrain neural operators on unlabeled PDE data using reconstruction-based proxy tasks.
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework.
Topological Regularization via Persistence-Sensitive Optimization
Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting.
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries.
Graph Representation Learning
Protein Function Prediction
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Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Machine learning has emerged as a powerful approach in materials discovery.
BIG-bench Machine Learning
Interpretable Machine Learning
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Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
Our results not only show a considerable improvement compared to the baseline, but they also highlight that topological features capture information complementary to the structural features: this is especially important for the adsorption at low pressure, a task particularly difficult for the traditional features.
Cosmic Inference: Constraining Parameters With Observations and Highly Limited Number of Simulations
Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters which characterize the underlying physical system -- our Universe.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data.
Dualities in persistent (co)homology
We consider sequences of absolute and relative homology and cohomology groups that arise naturally for a filtered cell complex.
Algebraic Topology Computational Geometry
