9 citations · 12 across the 4 of their papers we have counts for
5 papers
ISAAC Newton: Input-based Approximate Curvature for Newton's Method
Felix Petersen, Tobias Sutter, Christian Borgelt +4
We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing comput…
The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization
Dongsung Huh, Avinash Baidya
Machine learning models often generalize poorly to out-of-distribution (OOD) data as a result of relying on features that are spuriously correlated with the label during training.…
Generalized Phase Representation of Integrate-and-Fire Models
Dongsung Huh
The quadratic integrate-and-fire (QIF) model captures the normal form bifurcation dynamics of Type-I neurons found in cortex. Remarkably, this model is known to have a dual equival…
Gradient Descent for Spiking Neural Networks
Dongsung Huh, Terrence J. Sejnowski
Much of studies on neural computation are based on network models of static neurons that produce analog output, despite the fact that information processing in the brain is predomi…
The Vector Space of Convex Curves: How to Mix Shapes
Dongsung Huh
We present a novel, log-radius profile representation for convex curves and define a new operation for combining the shape features of curves. Unlike the standard, angle profile-ba…