Search Results for author:
Found 12 papers, 1 papers with code
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance of the models trained under them.
Online Loss Function Learning
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model.
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained under them.
Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data
In this paper, we propose to use StyleGAN to perform face alignment with limited training data instead of image generation.
Ranked #3 on
Face Alignment
on AFLW-19
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data
Supervised face alignment methods need large amounts of training data to achieve good performance in terms of accuracy and generalization.
Ranked #30 on
Face Alignment
on WFLW
Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems
The dialog history is represented in the form of dialog history embedding vectors (so-called h-vectors) and is provided as an additional information to end-to-end SLU models in order to improve the system performance.
IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification
This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge.
Participation de l'IRISA \`a DeFT 2018 : classification et annotation d'opinion dans des tweets (IRISA at DeFT 2018: classifying and tagging opinion in tweets )
L{'}{\'e}quipe a particip{\'e} {\`a} 3 des 4 t{\^a}ches de la campagne : (i) classification des tweets selon s{'}ils concernent les transports ou non, (ii) classification des tweets selon leur polarit{\'e} et (iii) annotation des marqueurs d{'}opinion et de l{'}objet {\`a} propos duquel est exprim{\'e}e l{'}opinion.
Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking
We show that GANs can be used for multimodal representation learning and that they provide multimodal representations that are superior to representations obtained with multimodal autoencoders.
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network
There is an inherent need for autonomous cars, drones, and other robots to have a notion of how their environment behaves and to anticipate changes in the near future.
