282 research outputs found
Loop Space Formalism and K-Theoretic Quantum Serre Duality
In this paper, we prove the quantum Serre duality for genus-zero K-theoretic
permutation-invariant Gromov-Witten theory. The formulation of the theorem
relies on an extension to the formalism of loop spaces and big
-functions more intrinsic to quantum K-theory. With the extended
formalism, we also arrive at a re-interpretation of the level structures in
terms of twisted quantum K-theories. We discuss the torus-equivariant theory in
the end, and as an application generalize the K-theoretic quantum Serre duality
to non-primitive vector bundles over flag varieties.Comment: 42 pages, comments welcome
Rare Feature Selection in High Dimensions
It is common in modern prediction problems for many predictor variables to be
counts of rarely occurring events. This leads to design matrices in which many
columns are highly sparse. The challenge posed by such "rare features" has
received little attention despite its prevalence in diverse areas, ranging from
natural language processing (e.g., rare words) to biology (e.g., rare species).
We show, both theoretically and empirically, that not explicitly accounting for
the rareness of features can greatly reduce the effectiveness of an analysis.
We next propose a framework for aggregating rare features into denser features
in a flexible manner that creates better predictors of the response. Our
strategy leverages side information in the form of a tree that encodes feature
similarity.
We apply our method to data from TripAdvisor, in which we predict the
numerical rating of a hotel based on the text of the associated review. Our
method achieves high accuracy by making effective use of rare words; by
contrast, the lasso is unable to identify highly predictive words if they are
too rare. A companion R package, called rare, implements our new estimator,
using the alternating direction method of multipliers.Comment: 42 pages, 10 figure
Rumba : a Python framework for automating large-scale recursive internet experiments on GENI and FIRE+
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to remove the unimportant filters from a well-trained CNN, which estimates the filters’ importance by ablating them in turn and evaluating the model, thus delivers high accuracy but suffers from intolerable time complexity, and requires a given resulting width but cannot automatically find it. To address these problems, we propose Approximated Oracle Filter Pruning (AOFP), which keeps searching for the least important filters in a binary search manner, makes pruning attempts by masking out filters randomly, accumulates the resulting errors, and finetunes the model via a multi-path framework. As AOFP enables simultaneous pruning on multiple layers, we can prune an existing very deep CNN with acceptable time cost, negligible accuracy drop, and no heuristic knowledge, or re-design a model which exerts higher accuracy and faster inferenc
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