arXiv research

A spiking neural network model for probabilistic inference of binary Markov random fields.

The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary distributions using a piecewise differentiable augmentation. We illust…

Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …

Study of quantum Riemannian geometries over binary field, finding many non-flat examples.

Study shows proper initialisation of binary weights is crucial for deep neural networks.

Estimates binary labels from dependent data using Markov Random Fields.

We investigate a class of binary choice models with social interactions. We propose a unifying perspective that integrates economic models using a utility function and psychological models using an impact function. A general approach for analyzing the equilibrium structure of these models within mean-field approximatio…

Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems. In this work, we present a data-driven approach for the learning, modeling, and prediction of phase segregation. A direct …

Machine learning struggles to predict binary options movements due to randomness.

BEGIN network models binary data without parametric assumptions.

Gravitational waves are predicted by the general theory of relativity. In [6] D. Christodoulou showed that gravitational waves have a nonlinear memory. We proved in [3] that the electromagnetic field contributes at highest order to the nonlinear memory effect of gravitational waves. In the present paper, we study this …

We construct an elementary, combinatorial kind of topological quantum field theory, based on curves, surfaces, and orientations. The construction derives from contact invariants in sutured Floer homology and is essentially an elaboration of a TQFT defined by Honda--Kazez--Matic. This topological field theory stores inf…

Efficiently identifies important variables in binary outcomes using variational Bayes.

Study geodesics in curved spaces, counts ambiguous paths, confirms number theory conjectures.

New method predicts binary matrix entries using empirical Bayes and low-rank structure.

Binary autoencoder with sparse hidden layer preserves information and zero reconstruction error.

New algorithm for faster feature enumeration over finite fields.

Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established…

We present a new approach for mitigating unfairness in learned classifiers. In particular, we focus on binary classification tasks over individuals from two populations, where, as our criterion for fairness, we wish to achieve similar false positive rates in both populations, and similar false negative rates in both po…

Gaussian CRFBC model for binary classification with latent variables.

Unified theory for training neural networks with binary synapses.

MCD reformulates conditional density estimation into binary classification.

Proposes MCC-F1 curve for better binary classification evaluation.

Special orthogonal representations from octonions have geometric properties linked to binary cubics.

AFS-BM improves model accuracy by dynamically selecting features.

We introduce the notions of Atiyah class and Todd class of a differential graded vector bundle with respect to a differential graded Lie algebroid. We prove that the space of vector fields on a dg-manifold with homological vector field $Q$ admits a structure of L-infinity algebra with the Lie derivative $L_Q$ as unary …

Proposes MRIV framework for unbiased CATE estimation using binary IVs.

We show that over the binary field $\mathbb F_2$, the Bar-Natan perturbation of Khovanov homology splits as the direct sum of its two reduced theories, which we also prove are isomorphic. This extends Shumakovitch's analogous result for ordinary Khovanov homology, without the perturbation.

A new algebraic structure emerges from reductive homogeneous spaces.

The paper analyzes the score field of diffusion models using Burgers dynamics.

Interactive tool helps choose and understand classification metrics.

Over the $(1,n)$-dimensional real superspace, $n>1$, we classify $\mathcal{K}(n)$-invariant binary differential operators acting on the superspaces of weighted densities, where $\mathcal{K}(n)$ is the Lie superalgebra of contact vector fields. This result allows us to compute the first differential cohomology of %the L…

Develops a fast BMF approach for binary matrices.

Novel link classification connects quadratic forms and knot theory.

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…

We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler to estimate the log-partition function of restricted Boltzmann ma…

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…

Sequential or online dimensional reduction is of interests due to the explosion of streaming data based applications and the requirement of adaptive statistical modeling, in many emerging fields, such as the modeling of energy end-use profile. Principal Component Analysis (PCA), is the classical way of dimensional redu…

Paper clarifies unary vs binary AV approaches and evaluates their performance.

Paper proposes neural networks for automatically naming assembly functions.

Research classifies quadratic forms over various fields.

This paper applies deep learning to ordinal regression, modeling it as a binary search.

Significant success has been reported recently using deep neural networks for classification. Such large networks can be computationally intensive, even after training is over. Implementing these trained networks in hardware chips with a limited precision of synaptic weights may improve their speed and energy efficienc…

A new uplift modeling approach uses binary treatment indicators more efficiently.

PoET-BiN reduces power consumption in neural networks on embedded devices.

The paper uses machine learning to detect malicious executable files.

We study the persistence phenomenon in a socio-econo dynamics model using computer simulations at a finite temperature on hypercubic lattices in dimensions up to 5. The model includes a ` social\rq local field which contains the magnetization at time $t$. The nearest neighbour quenched interactions are drawn from a bin…

Survey on assessing and improving classifier calibration for better decision making.