arXiv research

We say that a pair of points x and y is secure if there exist a finite set of blocking points such that any geodesic between x and y passes through one of the blocking points. The main point of this paper is to exhibit new examples of blocking phenomena both in the manifold and the billiard table setting. As an approac…

THieF improves day-ahead electricity price prediction accuracy by reconciling hourly and block forecasts.

Proposes RBGP framework for efficient block sparse neural networks.

New algorithms improve community detection and parameter estimation for PABM.

Power of network tests degrades when vertices are misaligned.

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is often smaller than the ambient dimension. Spectral clustering, as one of the main approaches to subspace clustering, often takes on a sparse representation or a low-rank represent…

New algorithm for nonconvex optimization on constrained Riemannian manifolds converges quickly.

Extends random dot product graph model to handle multiple graphs.

Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network model known as the random dot product graph, to allow interpretation of those vector representations as latent position estimates. The general…

Geometrically connects theta functions and WZNW blocks.

New solver MPLP++ outperforms existing solvers for dense graph models.

Study shows AMM liquidity providers lose more than they earn, with varying profitability across pairs.

New method for deep learning hierarchies like sequences and graphs.

We derive relations between theoretical properties of restricted Boltzmann machines (RBMs), popular machine learning models which form the building blocks of deep learning models, and several natural notions from discrete mathematics and convex geometry. We give implications and equivalences relating RBM-representable …

Study on determinants of unitary Brownian motion and their asymptotic laws.

We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector; these vectors are clustere…

Recursive Feature Machines show grokking in modular arithmetic without neural networks.

Let $φ: M\to M$ be a diffeomorphism of a $C^\infty$ compact connected manifold, and $X$ its mapping torus. There is a natural fibration $p:X\to S^1$, denote by $ξ\in H^1(X, \mathbb{Z})$ the corresponding cohomology class. Let $λ\in \mathbb{Z}^*$. Consider the endomorphism $φ_k^*$ induced by $φ$ in the cohomology of $M$…

DIAL learns embeddings to maximize recall and accuracy for entity resolution.

Tests if vertices in graphs have the same latent positions.

Large-scale L1-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. High-performance algorithms and implementations are critical to efficiently solving these problems. Building…

New algorithms tackle complex multi-block optimization problems in machine learning.

Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators,…

Tractable yet expressive density estimators are a key building block of probabilistic machine learning. While sum-product networks (SPNs) offer attractive inference capabilities, obtaining structures large enough to fit complex, high-dimensional data has proven challenging. In this paper, we present random sum-product …

A new method for spectral positional encodings in directed graphs using Hermitian block Krylov subspaces.

Graphical notation simplifies tensor operations and decompositions.

In this paper we analyse the bipartite Colombian firms-products network, throughout a period of five years, from 2010 to 2014. Our analysis depicts a strongly modular system, with several groups of firms specializing in the export of specific categories of products. These clusters have been detected by running the bipa…

A new tensor network method for image classification reduces computation cost.

Online shopping caters to the needs of millions of users daily. Search, recommendations, personalization have become essential building blocks for serving customer needs. Efficacy of such systems is dependent on a thorough understanding of products and their representation. Multiple information sources and data types p…

The paper corrects for node degree in spectral clustering using random walk Laplacian.

Efficient algorithm for robust recovery in stochastic block models.

New method for directed graphs using learnable spectral positional encodings.

New formula classifies product reviews into higher and lower ratings based on sentiment analysis.

The paper explores warped-like product metrics with exceptional holonomy groups.

Unsupervised deep learning is one of the most powerful representation learning techniques. Restricted Boltzman machine, sparse coding, regularized auto-encoders, and convolutional neural networks are pioneering building blocks of deep learning. In this paper, we propose a new building block -- distributed random models…

Enhanced EEG classification improves motor imagery detection with less computation.

Are large scale research programs that include many projects more productive than smaller ones with fewer projects? This problem of economy of scale is particularly relevant for understanding recent mergers in particular in the pharmaceutical industry. We present a quantitative theory based on the characterization of d…

We prove a central limit theorem for the components of the eigenvectors corresponding to the $d$ largest eigenvalues of the normalized Laplacian matrix of a finite dimensional random dot product graph. As a corollary, we show that for stochastic blockmodel graphs, the rows of the spectral embedding of the normalized La…

Graph learning categorizes DeFi services into similar functionalities.

Volterra series are especially useful for nonlinear system identification, also thanks to their capability to approximate a broad range of input-output maps. However, their identification from a finite set of data is hard, due to the curse of dimensionality. Recent approaches have shown how regularized kernel-based met…

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assu…

Additive decoders tackle latent variables and image generation.

New fusion blocks improve equivariant neural networks for molecular dynamics.

Let $φ: M\to M$ be a diffeomorphism of a $C^\infty$ compact connected manifold, and $X$ its mapping torus. There is a natural fibration $p:X\to S^1$, denote by $ξ\in H^1(X, \mathbb{Z})$ the corresponding cohomology class. Let $ρ:π_1(X)\to GL(n,\mathbb{C})$ be a representation, denote by $H^*(X,ρ)$ the corresponding twi…

Develops efficient quasi-Newton methods for training deep neural networks.

We show that Ozsváth-Szabó's bordered algebra used to efficiently compute knot Floer homology is a graded flat deformation of the regular block of a $\mathfrak{q}$-presentable quotient of parabolic category $\mathcal{O}$. We identify the endomorphism algebra of a minimal projective generator for this block with an expl…

Paper proposes new gradient codes for robust distributed machine learning.

Motivated by community detection, we characterise the spectrum of the non-backtracking matrix $B$ in the Degree-Corrected Stochastic Block Model. Specifically, we consider a random graph on $n$ vertices partitioned into two equal-sized clusters. The vertices have i.i.d. weights $\{ φ_u \}_{u=1}^n$ with second moment $Φ…