30 citations · 62 across the 5 of their papers we have counts for
5 papers
Soft Calibration Objectives for Neural Networks
Archit Karandikar, Nicholas Cain, Dustin Tran +4
Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-con…
Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens +1
For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence an…
Feedback through graph motifs relates structure and function in complex networks
Yu Hu, Steven L. Brunton, Nicholas Cain +3
In physics, biology and engineering, network systems abound. How does the connectivity of a network system combine with the behavior of its individual components to determine its c…
Impact of correlated neural activity on decision making performance
Nicholas Cain, Eric Shea-Brown
Stimulus from the environment that guides behavior and informs decisions is encoded in the firing rates of neural populations. Each neuron in the populations, however, does not spi…
Neural integrators for decision making: A favorable tradeoff between robustness and sensitivity
Nicholas Cain, Andrea K. Barreiro, Michael Shadlen +1
A key step in many perceptual decision tasks is the integration of sensory inputs over time, but fundamental questions remain about how this is accomplished in neural circuits. One…