Search Results for author:
Found 6 papers, 3 papers with code
Pretrained Knowledge Base Embeddings for improved Sentential Relation Extraction
In this work we put forward to combine pretrained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing.
Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models
In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings.
Product Jacobi-Theta Boltzmann machines with score matching
The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques.
Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units.
Sampling the Riemann-Theta Boltzmann Machine
We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians.
Riemann-Theta Boltzmann Machine
Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques.
