43 citations · 52 across the 8 of their papers we have counts for
9 papers
Generative AI-Based Virtual Assistant using Retrieval-Augmented Generation: An evaluation study for bachelor projects
Dumitru Verşebeniuc, Martijn Elands, Sara Falahatkar +4
Large Language Models have been increasingly employed in the creation of Virtual Assistants due to their ability to generate human-like text and handle complex inquiries. While the…
Capacity Matters: a Proof-of-Concept for Transformer Memorization on Real-World Data
Anton Changalidis, Aki Härmä
This paper studies how the model architecture and data configurations influence the empirical memorization capacity of generative transformers. The models are trained using synthet…
Fully Autonomous Programming using Iterative Multi-Agent Debugging with Large Language Models
Anastasiia Grishina, Vadim Liventsev, Aki Härmä +1
Program synthesis with Large Language Models (LLMs) suffers from a "near-miss syndrome": the generated code closely resembles a correct solution but fails unit tests due to minor e…
Survey on biomarkers in human vocalizations
Aki Härmä, Bert den Brinker, Ulf Grossekathofer +4
Recent years has witnessed an increase in technologies that use speech for the sensing of the health of the talker. This survey paper proposes a general taxonomy of the technologie…
Sensing of inspiration events from speech: comparison of deep learning and linguistic methods
Aki Härmä, Ulf Grossekathöfer, Okke Ouweltjes +1
Respiratory chest belt sensor can be used to measure the respiratory rate and other respiratory health parameters. Virtual Respiratory Belt, VRB, algorithms estimate the belt senso…
Fully Autonomous Programming with Large Language Models
Vadim Liventsev, Anastasiia Grishina, Aki Härmä +1
Current approaches to program synthesis with Large Language Models (LLMs) exhibit a "near miss syndrome": they tend to generate programs that semantically resemble the correct answ…
A Survey on Human-aware Robot Navigation
Ronja Möller, Antonino Furnari, Sebastiano Battiato +2
Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical man…
Neurogenetic Programming Framework for Explainable Reinforcement Learning
Vadim Liventsev, Aki Härmä, Milan Petković
Automatic programming, the task of generating computer programs compliant with a specification without a human developer, is usually tackled either via genetic programming methods…
BF++: a language for general-purpose program synthesis
Vadim Liventsev, Aki Härmä, Milan Petković
Most state of the art decision systems based on Reinforcement Learning (RL) are data-driven black-box neural models, where it is often difficult to incorporate expert knowledge int…