1 citations · 1 across the 7 of their papers we have counts for
7 papers
Atom dimension adaptation for infinite set dictionary learning
Andra Băltoiu, Denis C. Ilie-Ablachim, Bogdan Dumitrescu
Recent work on dictionary learning with set-atoms has shown benefits in anomaly detection. Instead of viewing an atom as a single vector, these methods allow building sparse repres…
"Vorbeşti Româneşte?" A Recipe to Train Powerful Romanian LLMs with English Instructions
Mihai Masala, Denis C. Ilie-Ablachim, Alexandru Dima +10
In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the trai…
OpenLLM-Ro -- Technical Report on Open-source Romanian LLMs
Mihai Masala, Denis C. Ilie-Ablachim, Dragos Corlatescu +6
In recent years, Large Language Models (LLMs) have achieved almost human-like performance on various tasks. While some LLMs have been trained on multilingual data, most of the trai…
Anomaly Detection with Selective Dictionary Learning
Denis C. Ilie-Ablachim, Bogdan Dumitrescu
In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL). The main contribution consists in the adaption of…
Reduced Kernel Dictionary Learning
Denis C. Ilie-Ablachim, Bogdan Dumitrescu
In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL has the drawback of a larg…
Classification with Incoherent Kernel Dictionary Learning
Denis C. Ilie-Ablachim, Bogdan Dumitrescu
In this paper we present a new classification method based on Dictionary Learning (DL). The main contribution consists of a kernel version of incoherent DL, derived from its standa…
Kernel t-distributed stochastic neighbor embedding
Denis C. Ilie-Ablachim, Bogdan Dumitrescu, Cristian Rusu
This paper presents a kernelized version of the t-SNE algorithm, capable of mapping high-dimensional data to a low-dimensional space while preserving the pairwise distances between…