Search Results for author:
Found 8 papers, 2 papers with code
A Survey of Deep Learning and Foundation Models for Time Series Forecasting
Furthermore, there is a vast amount of knowledge available that deep learning models can tap into, including Knowledge Graphs and Large Language Models fine-tuned with scientific domain knowledge.
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer Learning
Deep transfer learning techniques try to tackle the limitations of deep learning, the dependency on extensive training data and the training costs, by reusing obtained knowledge.
Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML
However, advances in machine learning research indicate that neural networks can be powerful data modeling techniques, as they can give higher accuracy for a plethora of learning problems and datasets.
Stereotype-Free Classification of Fictitious Faces
The proposed approach aids labeling new data (fictitious output images) by minimizing a penalized version of the least squares cost function between realistic pictures and target pictures.
Video Contents Understanding using Deep Neural Networks
We propose a novel application of Transfer Learning to classify video-frame sequences over multiple classes.
GELATO and SAGE: An Integrated Framework for MS Annotation
The first, is a free, semi-automated MSn data interpreter called the Glycomic Elucidation and Annotation Tool (GELATO).
Mining Massive Hierarchical Data Using a Scalable Probabilistic Graphical Model
In this paper we introduce an extension to Bayesian Networks to handle massive sets of hierarchical data in a reasonable amount of time and space.
PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical Data Problems
When modeling this kind of hierarchical data across large data sets, Bayesian networks become infeasible for representing the probability distributions for the following reasons: i) Each level represents a single random variable with hundreds of thousands of values, ii) The number of levels is usually small, so there are also few random variables, and iii) The structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes, so the network would contain a single linear path for the random variables from each parent to each child node.
