3 citations · 5 across the 5 of their papers we have counts for
5 papers
Inference from Real-World Sparse Measurements
Arnaud Pannatier, Kyle Matoba, François Fleuret
Real-world problems often involve complex and unstructured sets of measurements, which occur when sensors are sparsely placed in either space or time. Being able to model this irre…
Efficiently Training Low-Curvature Neural Networks
Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju +1
The highly non-linear nature of deep neural networks causes them to be susceptible to adversarial examples and have unstable gradients which hinders interpretability. However, exis…
The Theoretical Expressiveness of Maxpooling
Kyle Matoba, Nikolaos Dimitriadis, François Fleuret
Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest…
Challenges for Using Impact Regularizers to Avoid Negative Side Effects
David Lindner, Kyle Matoba, Alexander Meulemans
Designing reward functions for reinforcement learning is difficult: besides specifying which behavior is rewarded for a task, the reward also has to discourage undesired outcomes.…
A Computable Figure of Merit for Quasi-Monte Carlo Point Sets
Makoto Matsumoto, Mutsuo Saito, Kyle Matoba
Let be a finite point set of cardinality in an -dimensional cube, and let be an integrable function. A QMC integrati…