2 citations · 7 across the 13 of their papers we have counts for
7 papers · 1 filter
Which bits went where? Past and future transfer entropy decomposition with the information bottleneck
Kieran A. Murphy, Zhuowen Yin, Dani S. Bassett
Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of informa…
Comparing the information content of probabilistic representation spaces
Kieran A. Murphy, Sam Dillavou, Dani S. Bassett
Probabilistic representation spaces convey information about a dataset and are shaped by factors such as the training data, network architecture, and loss function. Comparing the i…
Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck
Kieran A. Murphy, Dani S. Bassett
Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution wit…
Intrinsically motivated graph exploration using network theories of human curiosity
Shubhankar P. Patankar, Mathieu Ouellet, Juan Cervino +3
Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a grap…
Information decomposition in complex systems via machine learning
Kieran A. Murphy, Dani S. Bassett
One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscop…
Interpretability with full complexity by constraining feature information
Kieran A. Murphy, Dani S. Bassett
Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the e…
Characterizing information loss in a chaotic double pendulum with the Information Bottleneck
Kieran A. Murphy, Dani S. Bassett
A hallmark of chaotic dynamics is the loss of information with time. Although information loss is often expressed through a connection to Lyapunov exponents -- valid in the limit o…