Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Tegner, Jesper"'
Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits
Quantum Machine Learning (QML) offers tremendous potential but is currently limited by the availability of qubits. We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC). This techn
Externí odkaz:
http://arxiv.org/abs/2411.08552
Autor:
Ozelim, Luan, Uthamacumaran, Abicumaran, Abrahão, Felipe S., Hernández-Orozco, Santiago, Kiani, Narsis A., Tegnér, Jesper, Zenil, Hector
We respond to arguments against our criticism that claim to show a divergence of Assembly Theory from popular compression. We have proven that any implementation of the concept of `copy number' underlying Assembly Theory (AT) and its assembly index (
Externí odkaz:
http://arxiv.org/abs/2408.15108
This paper studies the usefulness of incorporating path information in predicting chemical properties from molecular graphs, in the domain of QSAR (Quantitative Structure-Activity Relationship). Towards this, we developed a GNN-style model which can
Externí odkaz:
http://arxiv.org/abs/2407.14270
Autor:
Abrahão, Felipe S., Hernández-Orozco, Santiago, Kiani, Narsis A., Tegnér, Jesper, Zenil, Hector
We prove the full equivalence between Assembly Theory (AT) and Shannon Entropy via a method based upon the principles of statistical compression renamed `assembly index' that belongs to the LZ family of popular compression algorithms (ZIP, GZIP, JPEG
Externí odkaz:
http://arxiv.org/abs/2403.06629
Autor:
Radhakrishnan, Srijith, Yang, Chao-Han Huck, Khan, Sumeer Ahmad, Kumar, Rohit, Kiani, Narsis A., Gomez-Cabrero, David, Tegner, Jesper N.
We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR). Our methodology leverages both acoustic information and external linguistic representations to generate accurate speech tr
Externí odkaz:
http://arxiv.org/abs/2310.06434
Autor:
Zenil, Hector, Tegnér, Jesper, Abrahão, Felipe S., Lavin, Alexander, Kumar, Vipin, Frey, Jeremy G., Weller, Adrian, Soldatova, Larisa, Bundy, Alan R., Jennings, Nicholas R., Takahashi, Koichi, Hunter, Lawrence, Dzeroski, Saso, Briggs, Andrew, Gregory, Frederick D., Gomes, Carla P., Rowe, Jon, Evans, James, Kitano, Hiroaki, King, Ross
Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access
Externí odkaz:
http://arxiv.org/abs/2307.07522
Autor:
Radhakrishnan, Srijith, Yang, Chao-Han Huck, Khan, Sumeer Ahmad, Kiani, Narsis A., Gomez-Cabrero, David, Tegner, Jesper N.
In this work, we explore Parameter-Efficient-Learning (PEL) techniques to repurpose a General-Purpose-Speech (GSM) model for Arabic dialect identification (ADI). Specifically, we investigate different setups to incorporate trainable features into a m
Externí odkaz:
http://arxiv.org/abs/2305.11244
Classical-to-Quantum Transfer Learning Facilitates Machine Learning with Variational Quantum Circuit
While Quantum Machine Learning (QML) is an exciting emerging area, the accuracy of the loss function still needs to be improved by the number of available qubits. Here, we reformulate the QML problem such that the approximation error (representation
Externí odkaz:
http://arxiv.org/abs/2306.03741
Autor:
Munoz, Juan, Balsamy, Subash, Bernal-Tamayo, Juan P., Balubaid, Ali, de Infante, Alberto Maillo Ruiz, Lagani, Vincenzo, Gomez-Cabrero, David, Kiani, Narsis A., Tegner, Jesper
Discovering non-linear dynamical models from data is at the core of science. Recent progress hinges upon sparse regression of observables using extensive libraries of candidate functions. However, it remains challenging to model hidden non-observable
Externí odkaz:
http://arxiv.org/abs/2304.02443
Autor:
Balubaid, Ali, Alsolami, Samhan, Kiani, Narsis A., Gomez-Cabrero, David, Li, Mo, Tegner, Jesper
Publikováno v:
In iScience 15 November 2024 27(11)
