Zobrazeno 1 - 10
of 194
pro vyhledávání: '"P. Valdenegro"'
Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for esti
Externí odkaz:
http://arxiv.org/abs/2412.15439
Estimating the Cost of Informal Care with a Novel Two-Stage Approach to Individual Synthetic Control
Autor:
Petrillo, Maria, Valdenegro, Daniel, Rahal, Charles, Zhang, Yanan, Pryce, Gwilym, Bennett, Matthew R.
Informal carers provide the majority of care for people living with challenges related to older age, long-term illness, or disability. However, the care they provide often results in a significant income penalty for carers, a factor largely overlooke
Externí odkaz:
http://arxiv.org/abs/2411.10314
Publikováno v:
Oldenburg, V., Cardenas-Cartagena, J., & Valdenegro-Toro, M. (2024). Forecasting smog clouds with deep learning: A proof-of-concept. In ICML 2024 AI for Science Workshop. https://openreview.net/forum?id=UQa2PEVHMF
In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & PM2.5) with meteorological covariates between two locations using vario
Externí odkaz:
http://arxiv.org/abs/2410.02759
Uncertainty Quantification in Machine Learning has progressed to predicting the source of uncertainty in a prediction: Uncertainty from stochasticity in the data (aleatoric), or uncertainty from limitations of the model (epistemic). Generally, each u
Externí odkaz:
http://arxiv.org/abs/2408.12175
The AI act is the European Union-wide regulation of AI systems. It includes specific provisions for general-purpose AI models which however need to be further interpreted in terms of technical standards and state-of-art studies to ensure practical co
Externí odkaz:
http://arxiv.org/abs/2408.11249
Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Implementing Neural Network classifiers can have high performance but can be
Externí odkaz:
http://arxiv.org/abs/2407.03241
Modelling uncertainty in Machine Learning models is essential for achieving safe and reliable predictions. Most research on uncertainty focuses on output uncertainty (predictions), but minimal attention is paid to uncertainty at inputs. We propose a
Externí odkaz:
http://arxiv.org/abs/2406.18787
Autor:
Groot, Tobias, Valdenegro-Toro, Matias
Language and Vision-Language Models (LLMs/VLMs) have revolutionized the field of AI by their ability to generate human-like text and understand images, but ensuring their reliability is crucial. This paper aims to evaluate the ability of LLMs (GPT4,
Externí odkaz:
http://arxiv.org/abs/2405.02917
Neuromorphic computing mimics computational principles of the brain in $\textit{silico}$ and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer supe
Externí odkaz:
http://arxiv.org/abs/2404.05858
Autor:
Mulye, Mihir, Valdenegro-Toro, Matias
Explanation methods help understand the reasons for a model's prediction. These methods are increasingly involved in model debugging, performance optimization, and gaining insights into the workings of a model. With such critical applications of thes
Externí odkaz:
http://arxiv.org/abs/2403.17224