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Concept

Seq2seq

Publications associées (20)

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Arnout Jan J Devos

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

EPFL2024

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

Devis Tuia, Marc Conrad Russwurm

The governing hydrological processes are expected to shift under climate change in the alpine regions of Switzerland. This raises the need for more adaptive and accurate methods to estimate river flow. In high-altitude catchments influenced by snow and gla ...

2023

Class Specific Feature Disentanglement and Text Embeddings for Multi-label Generalized Zero Shot CXR Classification

Seyedbehzad Bozorgtabar

Robustness of medical image classification models is limited by its exposure to the candidate disease classes. Generalized zero shot learning (GZSL) aims at correctly predicting seen and unseen classes and most current GZSL approaches have focused on the s ...

Springer International Publishing Ag2023

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Indaco Biazzo

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physic ...

Nature Portfolio2023

Interpretable Representation Learning and Evaluation for Abstractive Summarization

Andreas Thomas Marfurt

Abstractive summarization has seen big improvements in recent years, mostly due to advances in neural language modeling, language model pretraining, and scaling models and datasets. While large language models generate summaries that are fluent, coherent, ...

EPFL2023

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

Alexis Berne, Anne-Claire Marie Billault--Roux, Gionata Ghiggi

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 ...

COPERNICUS GESELLSCHAFT MBH2023

Matrix product states with backflow correlations

Giuseppe Carleo

By taking inspiration from the backflow transformation for correlated systems, we introduce a tensor network Ansatz which extends the well-established matrix product state representation of a quantum many-body wave function. This structure provides enough ...

AMER PHYSICAL SOC2022

WikiArtVectors: Style and Color Representations of Artworks for Cultural Analysis via Information Theoretic Measures

Bhargav Srinivasa Desikan

With the increase in massive digitized datasets of cultural artefacts, social and cultural scientists have an unprecedented opportunity for the discovery and expansion of cultural theory. The WikiArt dataset is one such example, with over 250,000 high qual ...

MDPI2022

Pedestrian 3D Bounding Box Prediction

Alexandre Massoud Alahi, Saeed Saadatnejad, Yi Zhou Ju

Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained pre ...

2022

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

Jean-Marc Odobez, Michael Villamizar

We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as in state-of-the-art RNN-based approach ...

IEEE COMPUTER SOC2021

Plasma confinement mode classification using a sequence-to-sequence neural network with attention

Alessandro Pau, Gino Marceca

In a typical fusion experiment, the plasma can have several possible confinement modes. At the tokamak a configuration variable, aside from the low (L) and high (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing me ...

IOP Publishing Ltd2021

Discovery of Slow Variables in a Class Of Multiscale Stochastic Systems Via Neural Networks

Jan Sickmann Hesthaven, Przemyslaw Zielinski

Finding a reduction of complex, high-dimensional dynamics to its essential, low-dimensional "heart" remains a challenging yet necessary prerequisite for designing efficient numerical approaches. Machine learning methods have the potential to provide a gene ...

2022

Learning-based Image Coding: Early Solutions Reviewing and Subjective Quality Evaluation

Touradj Ebrahimi, Pinar Akyazi

Nowadays, image and video are the data types that consume most of the resources of modern communication channels, both in fixed and wireless networks. Thus, it is vital to compress visual data as much as possible, while maintaining some target quality leve ...

SPIE-INT SOC OPTICAL ENGINEERING2021

Volumetric Transformer Networks

Sabine Süsstrunk, Mathieu Salzmann, Seungryong Kim

Existing techniques to encode spatial invariance within deep convolutional neural networks (CNNs) apply the same warping field to all the feature channels. This does not account for the fact that the individual feature channels can represent different sema ...

2020

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Pascal Fua, Mateusz Kozinski, Leonardo Citraro, Subeesh Vasu

Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal t ...

2020

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Jie Luo, Mengshi Qi

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

Dolaana Khovalyg, Amirreza Heidari

With improved insulation of building envelopes and the use of low-temperature space heating systems, the share of energy use for domestic hot water (DHW) production in buildings has increased significantly, and nearly become the most energy-expensive servi ...

2020

Combining Range and Direction for Improved Localization

Martin Vetterli, Adam James Scholefield, Gilles Baechler, Robin Scheibler, Frederike Dümbgen, Golnooshsadat Elhami, Miranda Krekovic

Self-localization of nodes in a sensor network is typically achieved using either range or direction measurements; in this paper, we show that a constructive combination of both improves the estimation. We propose two localization algorithms that make use ...

2018

Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Rachid Guerraoui, Anne-Marie Kermarrec, Antoine Rault

Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recomme ...

IEEE2018

Towards End-to-End Speech Recognition

Dimitri Palaz

Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...

EPFL2016