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Found 16 papers, 8 papers with code
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
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Spectral Modes of Network Dynamics Reveal Increased Informational Complexity Near Criticality
Recent complexity measures such as integrated information have sought to operationalize this problem taking a whole-versus-parts perspective, wherein one explicitly computes the amount of information generated by a network as a whole over and above that generated by the sum of its parts during state transitions.
Relational Forward Models for Multi-Agent Learning
The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them.
Beyond integrated information: A taxonomy of information dynamics phenomena
Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them.
Neurons and Cognition Data Analysis, Statistics and Probability
Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data
The broad concept of emergence is instrumental in various of the most challenging open scientific questions -- yet, few quantitative theories of what constitutes emergent phenomena have been proposed.
Deep active inference agents using Monte-Carlo methods
In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.
Causal blankets: Theory and algorithmic framework
We introduce a novel framework to identify perception-action loops (PALOs) directly from data based on the principles of computational mechanics.
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles
Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas.
Integrated information as a common signature of dynamical and information-processing complexity
The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress.
Towards an extended taxonomy of information dynamics via Integrated Information Decomposition
Complex systems, from the human brain to the global economy, are made of multiple elements that interact in such ways that the behaviour of the `whole' often seems to be more than what is readily explainable in terms of the `sum of the parts.'
Greater than the parts: A review of the information decomposition approach to causal emergence
Emergence is a profound subject that straddles many scientific disciplines, including the formation of galaxies and how consciousness arises from the collective activity of neurons.
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks
These findings open the door to new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies in the system's information dynamics.
Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics
Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance.
Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions
Models that rely solely on pairwise relationships often fail to capture the complete statistical structure of the complex multivariate data found in diverse domains, such as socio-economic, ecological, or biomedical systems.
Information decomposition reveals hidden high-order contributions to temporal irreversibility
Temporal irreversibility, often referred to as the arrow of time, is a fundamental concept in statistical mechanics.
