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Found 12 papers, 3 papers with code
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it
In recent times, advances in artificial intelligence (AI) and IoT have enabled seamless and viable maintenance of appliances in home and building environments.
Universum GANs: Improving GANs through contradictions
Limited availability of labeled-data makes any supervised learning problem challenging.
DOC3-Deep One Class Classification using Contradictions
This paper introduces the notion of learning from contradictions (a. k. a Universum learning) for deep one class classification problems.
Stabilizing Bi-Level Hyperparameter Optimization using Moreau-Yosida Regularization
This research proposes to use the Moreau-Yosida envelope to stabilize the convergence behavior of bi-level Hyperparameter optimization solvers, and introduces the new algorithm called Moreau-Yosida regularized Hyperparameter Optimization (MY-HPO) algorithm.
Multiclass Learning from Contradictions
We introduce the notion of learning from contradictions, a. k. a Universum learning, for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM).
On-Device Machine Learning: An Algorithms and Learning Theory Perspective
However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.).
Variable Metric Proximal Gradient Method with Diagonal Barzilai-Borwein Stepsize
Under this metric selection for VM-PG, the theoretical convergence is analyzed.
Single Class Universum-SVM
This paper extends the idea of Universum learning [1, 2] to single-class learning problems.
Improving Model Training by Periodic Sampling over Weight Distributions
In this paper, we explore techniques centered around periodic sampling of model weights that provide convergence improvements on gradient update methods (vanilla \acs{SGD}, Momentum, Adam) for a variety of vision problems (classification, detection, segmentation).
Multiclass Universum SVM
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM).
Universum Learning for Multiclass SVM
We introduce Universum learning for multiclass problems and propose a novel formulation for multiclass universum SVM (MU-SVM).
Universum Learning for SVM Regression
This paper extends the idea of Universum learning [18, 19] to regression problems.
