arXiv research

Deep model learns from labeled and unlabeled data for industrial soft sensing.

The paper develops a multi-unit soft sensing model for virtual flow meters that improves few-shot learning.

We give soft, quantitatively optimal extensions of the classical Sphere Theorem, Wilking's connectivity principle and Frankel's Theorem to the context of ${k}$-th Ricci curvature. The hypotheses are soft in the sense that they are satisfied on sets of metrics that are open in the $C^{2}$-topology.

Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…

We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically so…

Data driven soft sensor design has recently gained immense popularity, due to advances in sensory devices, and a growing interest in data mining. While partial least squares (PLS) is traditionally used in the process literature for designing soft sensors, the statistical literature has focused on sparse learners, such …

The problem of estimating a high-dimensional sparse vector $\boldsymbolθ \in \mathbb{R}^n$ from an observation in i.i.d. Gaussian noise is considered. The performance is measured using squared-error loss. An empirical Bayes shrinkage estimator, derived using a Bernoulli-Gaussian prior, is analyzed and compared with the…

Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of…

We present an alternative proof of the following fact: the hyperspace of compact closed subsets of constant width in $\mathbb R^n$ is a contractible Hilbert cube manifold. The proof also works for certain subspaces of compact convex sets of constant width as well as for the pairs of compact convex sets of constant rela…

Theory models nonlinear soft tissue elasticity and remodeling using extended Finsler geometry.

Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…

Soft labeling impacts OOD detection in neural networks.

Developed a new thresholding method that connects soft and hard thresholding.

Soft cells fill space without gaps, derived from minimal surfaces and deformed using edge bending.

ASBART accelerates Soft BART for faster Bayesian regression.

Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe instability when training on large off-policy data or cannot scale to tasks with very high state and action dimensionality such as 3D humanoid l…

New method improves stability of soft FQI for offline RL.

Generalizes soft noncommutative schemes to flag varieties.

Paper proposes a new loss function for conditional models using soft targets.

Introduces Soft-SVM for binary classification bridging logistic and SVM.

This paper introduces TNTK to study infinite soft tree ensembles.

Improves deep neural networks using soft labels through alternating minimization.

The present work extends the randomized shortest-paths framework (RSP), interpolating between shortest-path and random-walk routing in a network, in three directions. First, it shows how to deal with equality constraints on a subset of transition probabilities and develops a generic algorithm for solving this constrain…

In this paper we study the performance of the Projected Gradient Descent(PGD) algorithm for $\ell_{p}$-constrained least squares problems that arise in the framework of Compressed Sensing. Relying on the Restricted Isometry Property, we provide convergence guarantees for this algorithm for the entire range of $0\leq p\…

In this article supervised learning problems are solved using soft rule ensembles. We first review the importance sampling learning ensembles (ISLE) approach that is useful for generating hard rules. The soft rules are then obtained with logistic regression from the corresponding hard rules. In order to deal with the p…

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers fro…

Proposes a new method for deep ensembles that improves accuracy and calibration.

Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.

A method for selecting pseudo-labeled data in semi-supervised learning using generalized Bayes and soft revision.

Given a group $G$ and a class of manifolds $\CC$ (e.g. symplectic, contact, Kähler etc), it is an old problem to find a manifold $M_G \in \CC$ whose fundamental group is $G$. This article refines it: for a group $G$ and a positive integer $r$ find $M_G \in \CC$ such that $π_1(M_G)=G$ and $π_i(M_G)=0$ for $1<i<r$. We th…

Floer homology is a good example of homological invariants living in the infinite dimension. We suggest a way to construct this kind of invariants using only soft essentially finite-dimensional tools; no hard analysis or PDE is involved. This work is partially inspired by the M. Gromov's survey ``Soft and hard symplect…

New system uses wearable bio-signals for easy authentication.

In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter re…

MSLG generates soft labels to improve DNN performance on noisy datasets.

Study of bound states in quantum layers with confining potentials.

Constructs noncommutative spaces for D-branes on complex algebraic spaces.

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets includin…

HyperFair integrates fairness in recommender systems using probabilistic soft logic.

Partial soft-matching distance improves neural representation comparison by allowing some neurons to remain unmatched.

A framework learns dynamic soft labels to improve model generalization and accuracy.

New algorithms optimize a soft-robust criterion in reinforcement learning, reducing conservatism.

We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.

Soft geometric bias improves physical dynamics predictions.

We develop embeddings for nonlinear subspaces preserving vector norms.

VaSST uses soft symbolic trees for probabilistic symbolic regression.

LC-SAC tackles non-stationary dynamics in reinforcement learning.

Networked sensing, where the goal is to perform complex inference using a large number of inexpensive and decentralized sensors, has become an increasingly attractive research topic due to its applications in wireless sensor networks and internet-of-things. To reduce the communication, sensing and storage complexity, t…

LightGCNet simplifies AI for soft sensors, reducing complexity and training time.