Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Analyzing the convergence properties of these algorithms can be complex, especially for NAG whose convergence proof relies on algebraic tricks that reveal little detail about the acceleration phenomenon, i.e. the celebrated optimality of NAG in convex smooth optimization. Instead, an alternative approach is to view these methods as numerical integrators of some ordinary differential equations (ODEs).

This paper explores the problem of building ML systems that failloudly, investigating methods for detecting dataset shift, identifying exemplarsthat most typify the shift, and quantifying shift malignancy. We focus on severaldatasets and various perturbations to both covariates and label distributions withvarying magnitudes and fractions of data affected. Interestingly, we show thatacross the dataset shifts that we explore, a two-sample-testing-based approach,using pre-trained classifiers for dimensionality reduction, performs best.

Neural Information Processing Systems http://nips.cc/

We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.

While recent research hasproposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a completeentity.