Search Results for author:
Found 11 papers, 2 papers with code
ATD: Augmenting CP Tensor Decomposition by Self Supervision
This paper addresses the above challenges by proposing augmented tensor decomposition (ATD), which effectively incorporates data augmentations and self-supervised learning (SSL) to boost downstream classification.
MTC: Multiresolution Tensor Completion from Partial and Coarse Observations
Our MTC model explores tensor mode properties and leverages the hierarchy of resolutions to recursively initialize an optimization setup, and optimizes on the coupled system using alternating least squares.
SPARQ-SGD: Event-Triggered and Compressed Communication in Decentralized Stochastic Optimization
In this paper, we propose and analyze SPARQ-SGD, which is an event-triggered and compressed algorithm for decentralized training of large-scale machine learning models.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization
In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network.
QuPeL: Quantized Personalization with Applications to Federated Learning
When each client participating in the (federated) learning process has different requirements of the quantized model (both in value and precision), we formulate a quantized personalization framework by introducing a penalty term for local client objectives against a globally trained model to encourage collaboration.
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning
In this work, we introduce a \textit{quantized} and \textit{personalized} FL algorithm QuPeD that facilitates collective (personalized model compression) training via \textit{knowledge distillation} (KD) among clients who have access to heterogeneous data and resources.
Decentralized Multi-Task Stochastic Optimization With Compressed Communications
The paper develops algorithms and obtains performance bounds for two different models of local information availability at the nodes: (i) sample feedback, where each node has direct access to samples of the local random variable to evaluate its local cost, and (ii) bandit feedback, where samples of the random variables are not available, but only the values of the local cost functions at two random points close to the decision are available to each node.
Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management
Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings.
Alternating Mahalanobis Distance Minimization for Stable and Accurate CP Decomposition
Computing these critical points in an alternating manner motivates an alternating optimization algorithm which corresponds to alternating least squares algorithm in the matrix case.
Semantic rule Web-based Diagnosis and Treatment of Vector-Borne Diseases using SWRL rules
Afterwards, Basic Formal Ontology (BFO), National Vector Borne Disease Control Program (NVBDCP) guidelines, and RDF medical data are used to develop ontologies for VBDs, and Semantic Web Rule Language (SWRL) rules are applied for diagnosis and treatment.
Optical Character Recognition
Optical Character Recognition (OCR)
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression
Assuming a representation structure for the data generating linear models at the sources and the target domains, we propose a representation transfer based learning method for constructing the target model.
