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Found 16 papers, 10 papers with code
Efficiency Separation between RL Methods: Model-Free, Model-Based and Goal-Conditioned
We prove a fundamental limitation on the efficiency of a wide class of Reinforcement Learning (RL) algorithms.
A model-based approach to meta-Reinforcement Learning: Transformers and tree search
Meta-Reinforcement Learning (meta-RL) methods demonstrate a capability to learn behaviors that efficiently acquire and exploit information in several meta-RL problems.
PAC-learning gains of Turing machines over circuits and neural networks
We provide lower and upper bounds on the potential gains in sample efficiency between the MDL applied with Turing machines instead of ANNs.
Stochastic Thermodynamics of Non-Linear Electronic Circuits: A Realistic Framework for Computing around kT
We present a general formalism for the construction of thermodynamically consistent stochastic models of non-linear electronic circuits.
Statistical Mechanics
Severability of mesoscale components and local time scales in dynamical networks
A major goal of dynamical systems theory is the search for simplified descriptions of the dynamics of a large number of interacting states.
Multi-scale Anomaly Detection on Attributed Networks
Many social and economic systems can be represented as attributed networks encoding the relations between entities who are themselves described by different node attributes.
Social and Information Networks Physics and Society
Network constraints on the mixing patterns of binary node metadata
We consider the network constraints on the bounds of the assortativity coefficient, which measures the tendency of nodes with the same attribute values to be interconnected.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
Unsupervised Network Embedding for Graph Visualization, Clustering and Classification
In this work we provide an unsupervised approach to learn embedding representation for a collection of graphs so that it can be used in numerous graph mining tasks.
Measuring the effect of node aggregation on community detection
Many times the nodes of a complex network, whether deliberately or not, are aggregated for technical, ethical, legal limitations or privacy reasons.
Physics and Society Social and Information Networks
Multi-hop assortativities for networks classification
Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available.
Multiscale dynamical embeddings of complex networks
Complex systems and relational data are often abstracted as dynamical processes on networks.
Social and Information Networks Systems and Control Physics and Society
Modelling structure and predicting dynamics of discussion threads in online boards
We compare the efficiency of our approach with previous works and show its superiority for the prediction of the dynamics of discussions.
Social and Information Networks Probability Data Analysis, Statistics and Probability 90B18, 60K35, 60G55, 82C99
Positive semi-definite embedding for dimensionality reduction and out-of-sample extensions
In machine learning or statistics, it is often desirable to reduce the dimensionality of a sample of data points in a high dimensional space $\mathbb{R}^d$.
Dynamics Based Features For Graph Classification
Numerous social, medical, engineering and biological challenges can be framed as graph-based learning tasks.
Random Multi-Hopper Model. Super-Fast Random Walks on Graphs
Stated differently, for small parameter values the multi-hopper explores a general graph as fast as possible when compared to a random walker on a full graph.
Physics and Society Statistical Mechanics Social and Information Networks Mathematical Physics Mathematical Physics Probability
The many facets of community detection in complex networks
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years.
Social and Information Networks Data Analysis, Statistics and Probability Physics and Society
