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Found 15 papers, 8 papers with code
Partial information decomposition: redundancy as information bottleneck
The partial information decomposition (PID) aims to quantify the amount of redundant information that a set of sources provides about a target.
What it takes to solve the Origin(s) of Life: An integrated review of techniques
The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies.
Thermodynamics of Darwinian evolution in molecular replicators
We consider the relationship between thermodynamics, fitness, and Darwinian evolution in autocatalytic molecular replicators.
The Computational Capacity of LRC, Memristive and Hybrid Reservoirs
By introducing measures of the total linear and nonlinear computational capacities of the reservoir, we are able to design electronic circuits whose total computational capacity scales extensively with the system size.
A Novel Approach to the Partial Information Decomposition
We consider the "partial information decomposition" (PID) problem, which aims to decompose the information that a set of source random variables provide about a target random variable into separate redundant, synergistic, union, and unique components.
Decomposing information into copying versus transformation
In many real-world systems, information can be transmitted in two qualitatively different ways: by copying or by transformation.
Caveats for information bottleneck in deterministic scenarios
We demonstrate three caveats when using IB in any situation where $Y$ is a deterministic function of $X$: (1) the IB curve cannot be recovered by maximizing the IB Lagrangian for different values of $\beta$; (2) there are "uninteresting" trivial solutions at all points of the IB curve; and (3) for multi-layer classifiers that achieve low prediction error, different layers cannot exhibit a strict trade-off between compression and prediction, contrary to a recent proposal.
On the Information Bottleneck Theory of Deep Learning
The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior.
The Minor Fall, the Major Lift: Inferring Emotional Valence of Musical Chords through Lyrics
We investigate the association between musical chords and lyrics by analyzing a large dataset of user-contributed guitar tablatures.
Estimating Mixture Entropy with Pairwise Distances
We prove this family includes lower and upper bounds on the mixture entropy.
Nonlinear Information Bottleneck
Information bottleneck (IB) is a technique for extracting information in one random variable $X$ that is relevant for predicting another random variable $Y$.
Dynamics of beneficial epidemics
Third, in the context of dynamic social networks, we find that preferences for increased global infection accelerate spread and produce superexponential fixation, but preferences for local assortativity halt epidemics by disconnecting the infected from the susceptible.
Physics and Society Multiagent Systems Social and Information Networks Adaptation and Self-Organizing Systems Populations and Evolution
Modularity and the spread of perturbations in complex dynamical systems
We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations.
Physics and Society Social and Information Networks Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability
Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature
We used manually curated corpora of PubMed abstracts and annotated sentences to evaluate the efficacy of literature mining on two tasks: first, identifying PubMed abstracts containing pharmacokinetic evidence of DDIs; second, extracting sentences containing such evidence from abstracts.
Prediction and Modularity in Dynamical Systems
Identifying and understanding modular organizations is centrally important in the study of complex systems.
