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Found 11 papers, 6 papers with code
Simulating the economic impact of rationality through reinforcement learning and agent-based modelling
We find that RL agents spontaneously learn three distinct strategies for maximising profits, with the optimal strategy depending on the level of market competition and rationality.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs
In this work, we benchmark a number of search methods in the calibration of a well-known macroeconomic ABM on real data, and further assess the performance of "mixed strategies" made by combining different methods.
Intrinsic dimension estimation for discrete metrics
Real world-datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences.
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object.
DADApy: Distance-based Analysis of DAta-manifolds in Python
DADApy is a python software package for analysing and characterising high-dimensional data manifolds.
Redundant representations help generalization in wide neural networks
Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization performance.
Ranking the information content of distance measures
Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure.
Hierarchical nucleation in deep neural networks
This process leaves a footprint in the probability density of the output layer where the topography of the peaks allows reconstructing the semantic relationships of the categories.
SPONGE: A generalized eigenproblem for clustering signed networks
We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values.
Building machine learning force fields for nanoclusters
We assess Gaussian process (GP) regression as a technique to model interatomic forces in metal nanoclusters by analysing the performance of 2-body, 3-body and many-body kernel functions on a set of 19-atom Ni cluster structures.
Computational Physics
Efficient nonparametric $n$-body force fields from machine learning
We provide a definition and explicit expressions for $n$-body Gaussian Process (GP) kernels which can learn any interatomic interaction occurring in a physical system, up to $n$-body contributions, for any value of $n$.
Computational Physics
