9 citations · 9 across the 4 of their papers we have counts for
4 papers · 1 filter
World Machine: Towards Generative World Modeling for Time-Series
Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini +2
World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured and generalizable way. We pr…
What do vision-language models see in the context? Investigating multimodal in-context learning
Gabriel O. dos Santos, Esther Colombini, Sandra Avila
In-context learning (ICL) enables Large Language Models (LLMs) to learn tasks from demonstration examples without parameter updates. Although it has been extensively studied in LLM…
Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis
Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões +4
Understanding and controlling the informational complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and mo…
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages
Gabriel Oliveira dos Santos, Diego A. B. Moreira, Alef Iury Ferreira +9
This work introduces CAPIVARA, a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. While CLIP has excelled in zero…