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Found 7 papers, 2 papers with code
How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure
We find that LLMs perform well in generalizing the distribution of a novel noun argument between related contexts that were seen during pre-training (e. g., the active object and passive subject of the verb spray), succeeding by making use of the semantically-organized structure of the embedding space for word embeddings.
BioSimulators: a central registry of simulation engines and services for recommending specific tools
To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators. org), a central registry of the capabilities of simulation tools and consistent Python, command-line, and containerized interfaces to each version of each tool.
Do Language Models Learn Position-Role Mappings?
How is knowledge of position-role mappings in natural language learned?
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up
By ensuring that the Jacobian determinant can be computed efficiently, asymptotically exact sampling from the theory of interest can be performed by drawing samples from a simple distribution and passing them through the network.
A Topological Approach for Motion Track Discrimination
Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods to differentiate it from dynamic confusers present in the scene.
Machine learning determination of dynamical parameters: The Ising model case
We train a set of Restricted Boltzmann Machines (RBMs) on one- and two-dimensional Ising spin configurations at various values of temperature, generated using Monte Carlo simulations.
Computational Physics Statistical Mechanics High Energy Physics - Lattice
Accurate, fully-automated NMR spectral profiling for metabolomics
This information can be extracted from a biofluid's NMR spectrum.
