arXiv research

Sparse curves on surfaces grow at a specific intermediate rate.

The study finds sparse sets that uniquely determine metrics on negatively curved manifolds.

Simplified proof for dimension reduction of polygonal curves.

Paper proposes embedding models to capture semantic similarities of categorical attributes in financial bonds.

SAEs struggle with curved activation manifolds, revealing layer-dependent scaling laws.

Model shows loss curve with two distinct exponents due to sparse activations.

New techniques improve the accuracy of identifying nonlinear systems from noisy data.

US Yield curve has recently collapsed to its most flattened level since subprime crisis and is close to the inversion. This fact has gathered attention of investors around the world and revived the discussion of proper modeling and forecasting yield curve, since changes in interest rate structure are believed to repres…

Framework for transferring discount curve estimates across fixed-income product classes.

Neural network model improves robustness of mortgage bond yield curve estimation.

SGD learns sparse parities near computational limits with discontinuous phase transitions.

Deep model predicts shapes of curves with multiple covariates.

Study develops efficient algorithm for probabilistic penetration response of composite plates.

Study compares L1 and VG sparsity priors in inverse problems.

Study on theoretical limits of $\ell_0$ sparse-regression algorithms using Fl RDT.

Non-negative curvature affects Markov chains' mixing and expansion properties.

Unified framework identifies nonlinear systems using characteristic curves and neural networks.

Bayesian approach improves uncertainty in deep learning models.

Soft diamond regularizers improve deep learning performance and sparsity.

New algorithm optimizes AUC for sparse high-dimensional data in online learning.

In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assess…

Method improves SINDy for noisy nonlinear systems.

Characterizing the phase transitions of convex optimizations in recovering structured signals or data is of central importance in compressed sensing, machine learning and statistics. The phase transitions of many convex optimization signal recovery methods such as $\ell_1$ minimization and nuclear norm minimization are…

AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learni…

Develops method to assess feature importance in black-box models for unconditional distribution.

MINN-SA enhances cancer detection using TCR sequences with better interpretability.

We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, linear, or a low-co…

DiTSNe-Ia model accurately reconstructs supernovae spectra from light curves.

Building upon recent advances in entropy-regularized optimal transport, and upon Fenchel duality between measures and continuous functions , we propose a generalization of the logistic loss that incorporates a metric or cost between classes. Unlike previous attempts to use optimal transport distances for learning, our …

There are different problems for resolution of complex LC-MS or GC-MS data, such as the existence of embedded chromatographic peaks, continuum background and overlapping in mass channels for different components. These problems cause rotational ambiguity in recovered profiles calculated using multivariate curve resolut…

The paper explores algorithms to transform 3-manifold triangulations while controlling sparsity.

Traditionally in regression one minimizes the number of fitting parameters or uses smoothing/regularization to trade training (TE) and generalization error (GE). Driving TE to zero by increasing fitting degrees of freedom (dof) is expected to increase GE. However modern big-data approaches, including deep nets, seem to…

A study on the depth of graph neural networks on sparse graphs, revealing a dichotomy based on the Kesten-Stigum ratio.

Sparse Hopfield model improves memory retrieval with fewer connections.

We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teacher's couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the …

New quantum code lacks sparse lift.

In this paper, a sparse Markov decision process (MDP) with novel causal sparse Tsallis entropy regularization is proposed.The proposed policy regularization induces a sparse and multi-modal optimal policy distribution of a sparse MDP. The full mathematical analysis of the proposed sparse MDP is provided.We first analyz…

This work introduces a method to compare sparse neural network topologies using graph theory.

Sparse-RS framework efficiently attacks models with sparse perturbations.

Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…

Bayesian GAMs improve predictive performance for high-dimensional data.

Sparse deep neural networks(DNNs) are efficient in both memory and compute when compared to dense DNNs. But due to irregularity in computation of sparse DNNs, their efficiencies are much lower than that of dense DNNs on regular parallel hardware such as TPU. This inefficiency leads to poor/no performance benefits for s…

Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.

In compressed sensing, we wish to reconstruct a sparse signal $x$ from observed data $y$. In sparse coding, on the other hand, we wish to find a representation of an observed signal $y$ as a sparse linear combination, with coefficients $x$, of elements from an overcomplete dictionary. While many algorithms are competit…

We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations. The primary statistical properties of the sought signal is specified by the penalty function (i.e., negative logarithm of the prior probability density function). Our alternating direction m…

Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…

A closed hyperbolic surface of genus $g\ge 2$ can be decomposed into pairs of pants along shortest closed geodesics and if these curves are sufficiently short (and with lengths uniformly bounded away from 0), then the geometry of the surface is essentially determined by the combinatorics of the pants decomposition. The…

Dynamic Sparse Training finds efficient sparse networks from scratch.