arXiv research

A parsimonious model reduces over-parameterization in skewed matrix variate mixtures.

Proposes a low-rank bilinear pooling model for link prediction in knowledge graphs.

Over the years data has become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification. Although dimension reduction in the area of clus…

A new method clusters multi-view data by sharing a common trace-norm of coefficient matrices.

FiBiNET combines feature importance and bilinear interactions for CTR prediction.

This note improves on universal portfolios by using bilinear strategies.

Proposes a robust factor analysis for matrix data.

Deep Fusion improves audio-video emotion recognition accuracy.

FBSM improves item recommendation for cold-start users by modeling feature interactions.

New algorithm tackles bilinear bandit problem with low-rank structure.

Paper proposes method to recover quantized data with missing info.

In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-w…

MuLFA predicts drug interactions more accurately than existing methods.

Efficient algorithm converges to Nash equilibrium in bilinear problems with bandit feedback.

We propose a computationally efficient estimator, formulated as a convex program, for a broad class of non-linear regression problems that involve difference of convex (DC) non-linearities. The proposed method can be viewed as a significant extension of the "anchored regression" method formulated and analyzed in [10] f…

A new framework improves LSTM performance without adding more parameters.

We propose a doubly stochastic primal-dual coordinate optimization algorithm for empirical risk minimization, which can be formulated as a bilinear saddle-point problem. In each iteration, our method randomly samples a block of coordinates of the primal and dual solutions to update. The linear convergence of our method…

New algorithm reduces regret in graphical bilinear bandits.

Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known. The formulation counts sparse PCA with multiple factors, subspace clustering and low-rank sparse bilinear regression as potential ap…

The study generalizes twistor spinors to Kähler manifolds and finds bilinear form equations.

The paper studies harmonic symmetric bilinear forms on Riemannian manifolds and proves properties of the Bourguignon Laplacian.

Algorithm identifies bilinear dynamical systems from noisy data.

In this paper, we extend Su-Zhang's Cheeger-Mueller type theorem for symmetric bilinear torsions to manifolds with boundary in the case that the Riemannian metric and the non-degenerate symmetric bilinear form are of product structure near the boundary. Our result also extends Bruening-Ma's Cheeger-Mueller type theorem…

Bilinear MLPs offer a new way to interpret deep learning models without complex nonlinearities.

Identifies bilinear systems from a single trajectory with optimal sample complexity.

We present a new model, Predictive State Recurrent Neural Networks (PSRNNs), for filtering and prediction in dynamical systems. PSRNNs draw on insights from both Recurrent Neural Networks (RNNs) and Predictive State Representations (PSRs), and inherit advantages from both types of models. Like many successful RNN archi…

Generalizes Riemann's results on flat coordinates for non-symmetric bilinear forms.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

Enhances knot invariants using bilinear forms on vector spaces.

Constructs a bilinear form from a quasimorphism on symplectic manifold groups.

Shape adaptor learns flexible resizing factors for neural networks.

In this paper, we propose to employ a bank of modality-dedicated Convolutional Neural Networks (CNNs), fuse, train, and optimize them together for person classification tasks. A modality-dedicated CNN is used for each modality to extract modality-specific features. We demonstrate that, rather than spatial fusion at the…

We define a type of biquandle which is a generalization of symplectic quandles. We use the extra structure of these bilinear biquandles to define new knot and link invariants and give some examples.

Introduces and studies generalized B-opers with bilinear forms.

Gradient methods converge better for alternating updates in bilinear zero-sum games.

New algorithms improve robust PCA for vision tasks with heavy-tailed distributions.

Study learns linear system dynamics from noisy bilinear data.

We investigate the Gram determinant of the bilinear form based on curves in a planar surface, with a focus on the disk with two holes. We prove that the determinant based on $n-1$ curves divides the determinant based on $n$ curves. Motivated by the work on Gram determinants based on curves in a disk and curves in an an…

Non-bilinear observations make optimal control harder, showing non-convex costs and non-affine optimal controllers.

We use the Jones-Wenzl idempotents to construct a basis of Temperley-Lieb algebra TL_n. This allows a short calculation for a Gram determinant of Lickorish's bilinear form on the Temperley-Lieb algebra.

This thesis is concerned with the theory of invariant bilinear differential pairings on parabolic geometries. It introduces the concept formally with the help of the jet bundle formalism and provides a detailed analysis. More precisely, after introducing the most important notations and definitions, we first of all giv…

Unified bounds for sketched bilinear forms in machine learning and statistics.

Study examines metrics and functionals for Hodge-Dirac operator on manifolds.

BiN normalizes financial time-series for better forecasting.

Study dynamics of alternating minimization for bilinear regression under large system limits.

Novel hybrid bilinear model improves epilepsy diagnosis accuracy.

Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since associations among adjacent scalars are not modeled. In this paper, we propose a n…

In this paper the notion of an M-th order invariant bilinear differential pairing is introduced and a formal definition is given. If the manifold has an AHS structure, then various first order pairings are constructed. This yields a classification of all first order invariant bilinear differential pairings on homogeneo…