Julen Etxaniz
Hizkuntzaren Azterketa eta Prozesamendua Doktoregoko ikaslea
Euskal Herriko Unibertsitatea (UPV/EHU)
Informatika Fakultatea
Hizkuntzaren Azterketa eta Prozesamendua Doktoregoko ikaslea Euskal Herriko Unibertsitateko (UPV/EHU) Informatika Fakultatean. Informatika Ingeniaritzan graduatua Software Ingeniaritza espezialitatearekin. Hizkuntzaren Azterketa eta Prozesamendua Masterra.
Web honetan informazio hau aurkituko duzu: Trebetasunak, Ziurtagiriak, Proiektuak, Etiketak eta Kontaktua.
Esperientzia
Ziurtagiriak
Hizkuntzak
Programazio Lengoaiak
Web Garapena
Software Ingeniaritza
Ikasketa Automatikoa
Tresnak
Humans can learn to understand and process the distribution of space, and one of the initial tasks of Artificial Intelligence has been to show machines the relationships between space and the objects that appear in it. Humans naturally combine vision and textual information to acquire compositional and spatial relationships among objects, and when reading a text, we are able to mentally depict the spatial relationships that may appear in it. Thus, the visual differences between images depicting “a person sits and a dog stands” and “a person stands and a dog sits” are obvious for humans, but still not clear for automatic systems. In this project, we propose to evaluate grounded Neural Language models that can perform compositional and spatial reasoning. Neural Language models (LM) have shown impressive capabilities on many NLP tasks but, despite their success, they have been criticized for their lack of meaning. Vision-and-Language models (VLM), trained jointly on text and image data, have been offered as a response to such criticisms, but recent work has shown that these models struggle to ground spatial concepts properly. In the project, we evaluate state-of-the-art pre-trained and fine-tuned VLMs to understand their grounding level on compositional and spatial reasoning. We also propose a variety of methods to create synthetic datasets specially focused on compositional reasoning. We managed to accomplish all the objectives of this work. First, we improved the state-of-the-art in compositional reasoning. Next, we performed some zero-shot experiments on spatial reasoning. Finally, we explored three alternatives for synthetic dataset creation: text-to-image generation, image captioning and image retrieval. Code is released at https://github.com/juletx/spatial-reasoning and models are released at https://huggingface.co/juletxara.
Proiektuak
Image Caption Generation
Automatic Image Caption Generation model that uses a CNN to condition a LSTM based language model.
Shape Classification
The goal of the project is to compare different classification algorithms on the solution of plane and car shape datasets.
Academic Webgunea
Academic webgune pertsonala, atal hauek dituena: deskribapena, esteka sozialak, biografia, interesak, ikasketak, trebetasunak, esperientzia, lorpenak, proiektuak eta kontaktuko infomazioa.
Antxieta Arkeologi Taldea Webgunea
Antxieta Arkeologi Taldearen webgunea, gipuzkoan ikerketa arkeologikoa garatzen duen irabazi asmorik gabeko talde kulturala.
Community Detection
NIPS kongresuko autoreen komunitateak detektatzen metaheuristikoak erabiliz.
Comparing Writing Systems
Comparing Writing Systems with Multilingual Grapheme-to-Phoneme and Phoneme-to-Grapheme Conversion.
Deep Learning for Natural Language Processing
Deep Learning for Natural Language Processing slides, labs and assignments.
Dialbot
Ikasketa sakonean oinarritutako muturretik muturrerako solasaldi sistema.
Egunean Behin Visual Question Answering Dataset
This is a Visual Question Answering dataset based on questions from the game Egunean Behin. Egunean Behin is a popular Basque quiz game. The game consists on answering 10 daily multiple choice questions.
GitHub Webgunea
GitHub webgune pertsonala, atal hauek dituena: argazkia, deskribapen motza, esteka sozialak eta GitHub-eko errepositorioak eta gaiak.
Grounding Language Models for Spatial Reasoning
Grounding Language Models for Spatial Reasoning
Hyperpartisan News Analysis With Scattertext
Hyperpartisan News Analysis With Scattertext
Machine Learning and Neural Networks lectures
Machine Learning and Neural Networks lectures.
Mejorando la seguridad de mi web
Analizaré mi web con herramientas como Hardenize y Security Headers para detectar los aspectos de seguridad que se pueden mejorar.
NLP Applications I - Text Classification, Sequence Labelling, Opinion Mining and Question Answering
NLP Applications I - Text Classification, Sequence Labelling, Opinion Mining and Question Answering slides, labs and project.
NLP Applications II - Information Extraction, Question Answering, Recommender Systems and Conversational Systems
NLP Applications II - Information Extraction, Question Answering, Recommender Systems and Conversational Systems slides, labs and project.
Spiking Neural Network
Simulating the Izhikevich spiking neuron model using the Brian2 software
Zero-shot and Translation Experiments on XQuAD, MLQA and TyDiQA
Zero-shot and Translation Experiments on XQuAD, MLQA and TyDiQA
Kontaktua
- juletxara@gmail.com
- github.com/juletx
- linkedin.com/in/juletxara
- twitter.com/juletxara
- instagram.com/juletxara
- hackerrank.com/juletxara
- kaggle.com/juletxara
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Informatika Fakultatea
Manuel Lardizabal Ibilbidea, 1
20018 Donostia, Gipuzkoa, Espainia