STATNet: Spatial-temporal attention in the traffic prediction

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Self-attention mechanisms and non-local blocks have become crucial building blocks for state-of-the-art neural architectures thanks to their unparalleled ability in capturing long-range dependencies in the input. However their cost is quadratic with the nu ...

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how t ...

The way biological brains carry out advanced yet extremely energy efficient signal processing remains both fascinating and unintelligible. It is known however that at least some areas of the brain perform fast and low-cost processing relying only on a smal ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

We present a method for estimating the main properties of human skin, leveraging a hyperspectral dataset of skin tones synthetically generated through a biophysical layered skin model and Monte Carlo light transport simulations. Our approach learns the map ...

In this dissertation, we propose multiple methods to improve transfer learning for pretrained language models (PLMs). Broadly, transfer learning is a powerful technique in natural language processing, where a language model is first pre-trained on a data-r ...

End-to-end learning methods like deep neural networks have been the driving force in the remarkable progress of machine learning in recent years. However, despite their success, the deployment process of such networks in safety-critical use cases, such as ...

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

We introduce the first multitasking vision transformer adapters that learn generalizable task affinities which can be applied to novel tasks and domains. Integrated into an off-the-shelf vision transformer backbone, our adapters can simultaneously solve mu ...

This thesis consists of three applications of machine learning techniques to empirical asset pricing. In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural encoder-decoder design, combining convolutional and recurrent encoders together wi ...

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectr ...

Over the course of a lifetime, the human brain acquires an astonishing amount of semantic knowledge and autobiographical memories, often with an imprinting strong enough to allow detailed information to be recalled many years after the initial learning exp ...

In this study, we present the deep learning image segmentation model for drone-based grain size analysis of gravel bars called GALET. The objectives are to quantify the performance of the code and to test its applicability in river research and management. ...

STATNet: Spatial-temporal attention in the traffic prediction

Chat with Graph Search

Linear Complexity Self-Attention With 3rd Order Polynomials

The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

Supervised learning and inference of spiking neural networks with temporal coding

Spatially adaptive machine learning models for predicting water quality in Hong Kong

How deep convolutional neural networks lose spatial information with training

A Hyperspectral Space of Skin Tones for Inverse Rendering of Biophysical Skin Properties

Improving Generalization of Pretrained Language Models

Estimating and Improving the Robustness of Attributions in Text

Confidence Matters: Applications to Semantic Segmentation

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

A new age in protein design empowered by deep learning

Vision Transformer Adapters for Generalizable Multitask Learning

Essays in Empirical Asset Pricing

Modeling Structured Data in Attention-based Models

An end-to-end pipeline for historical censuses processing

Remote sensing visual question answering with a self-attention multi-modal encoder

Geographic clusters of objectively measured physical activity and the characteristics of their built environment in a Swiss urban area

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

A theory of memory consolidation and synaptic pruning in cortical circuits

GALET: A deep learning image segmentation model for drone-based grain size analysis of gravel bars