A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes BHT-ARIMA for forecasting multiple short time series.
We present a solution to scale spectral algorithms for learning sequence functions. We are interested in the case where these functions are sparse (that is, for most sequences they return 0). Spectral algorithms reduce the learning problem to the task of computing an SVD decomposition over a special type of matrix call…
New model mimics neural next item recommendation using Hankel matrices.
In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNNs): in the case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation functions are expressively equivalent. Motivated by this result, we build upon a recent ext…
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. Bu…
HSNLD solves robust Hankel recovery efficiently and robustly.
The paper tackles system identification via Hankel nuclear norm regularization, improving estimation rates and singular value gaps.
The paper reviews Hankel low-rank methods for time series analysis and forecasting.
Paper speeds up tensor factorization algorithms.
Spectral regularization simplifies sequence models by focusing on grammatical simplicity.
Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on -type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric met…
Algorithm learns graph operator from sparse space-time samples.
We consider the problem of identifying multiway block structure from a large noisy tensor. Such problems arise frequently in applications such as genomics, recommendation system, topic modeling, and sensor network localization. We propose a tensor block model, develop a unified least-square estimation, and obtain the t…
The annihilating filter-based low-rank Hankel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. The success of ALOHA is due to the concise signal representation in the k-space domain thanks to…
HOPE improves SSMs for long-memory tasks with robust initialization and training.
Paper proposes efficient methods for high-order clustering in tensor block models.
TGCCA analyzes higher-order tensors using orthogonal rank-R CP decomposition.
This work is devoted to elaboration on the idea to use block term decomposition for group data analysis and to raise the possibility of modelling group activity with (Lr, 1) and Tucker blocks. A new generalization of block tensor decomposition was considered in application to group data analysis. Suggested approach was…
Detects synchronized behavior in streaming data.
New method controls linear systems with adversarial disturbances.
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alternates between low-rank CP decomposition through gradient ascent (a variant of the tensor power method), and hard thresholding of the resid…
New fusion blocks improve equivariant neural networks for molecular dynamics.
The paper tackles estimation of hidden state LTI systems of unknown order.
Noise-robust Koopman operator framework for control with improved stability and performance.
New nonconvex methods improve SysID efficiency and accuracy.
By using variational calculus and exterior derivative formalism, we proposed in two previous joint papers with S. Siparov a new geometric approach for electromagnetism in pseudo-Finsler spaces. In the present paper, we provide more details, especially regarding generalized currents, the domain of integration and gauge …
New model analyzes customer churn with tensor completion and binary data.
This paper addresses network anomography, that is, the problem of inferring network-level anomalies from indirect link measurements. This problem is cast as a low-rank subspace tracking problem for normal flows under incomplete observations, and an outlier detection problem for abnormal flows. Since traffic data is lar…
Unified framework for coupled tensor completion improves recovery accuracy.
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources. Researchers resort to the re…
Paper speeds up GP inference by reducing precision matrix computation.
Community detection is the task of detecting hidden communities from observed interactions. Guaranteed community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we remove this restriction, and provide guaranteed community detection f…
TWIST algorithm detects communities in multi-layer networks with tensor decomposition.
We tackle tensor denoising with unknown permutations, achieving optimal recovery with polynomial estimators.
Nonnegative CANDECOMP/PARAFAC (NCP) decomposition is an important tool to process nonnegative tensor. Sometimes, additional sparse regularization is needed to extract meaningful nonnegative and sparse components. Thus, an optimization method for NCP that can impose sparsity efficiently is required. In this paper, we co…
A new tensor network method for image classification reduces computation cost.
New tensor model reduces GLM estimation error and sample complexity.
Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in …
Develops a new tensor model for clustering with degree correction.
The behavior under conformal change of the renormalized volume coefficients associated to a pseudo-Riemannian metric is investigated. It is shown that they define second order fully nonlinear operators in the conformal factor whose algebraic structure is elucidated via the introduction of "extended obstruction tensors"…
Improved tensor GLM estimation for complex data.
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits. The optimization dynamics is interpreted as moving in the steepest descent direction with respect to the Quantum Information Geometry, corresponding to the real part of…
A new probabilistic BTD method for tensor data.
Constructs new topological theories in 2D not fitting standard axioms.
Graphical notation simplifies tensor operations and decompositions.
Tensor networks improve image classification but require more expressive states.
Co-Clustering, the problem of simultaneously identifying clusters across multiple aspects of a data set, is a natural generalization of clustering to higher-order structured data. Recent convex formulations of bi-clustering and tensor co-clustering, which shrink estimated centroids together using a convex fusion penalt…
Seq2Tens uses tensors to efficiently represent sequences, improving performance on time series and video tasks.