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pro vyhledávání: '"Raymond, Christian"'
Autor:
Raymond, Christian
Humans can often quickly and efficiently solve complex new learning tasks given only a small set of examples. In contrast, modern artificially intelligent systems often require thousands or millions of observations in order to solve even the most bas
Externí odkaz:
http://arxiv.org/abs/2406.09713
The goal of few-shot learning is to generalize and achieve high performance on new unseen learning tasks, where each task has only a limited number of examples available. Gradient-based meta-learning attempts to address this challenging task by learn
Externí odkaz:
http://arxiv.org/abs/2406.07983
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. Specifically, we propose a new meta-
Externí odkaz:
http://arxiv.org/abs/2403.00865
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. Existing techniques for loss function learning have shown promising results, often improving a
Externí odkaz:
http://arxiv.org/abs/2301.13247
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. Specifically, we propose a new meta-learning framework for le
Externí odkaz:
http://arxiv.org/abs/2209.08907
Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems
Autor:
Tomashenko, Natalia, Raymond, Christian, Caubriere, Antoine, De Mori, Renato, Esteve, Yannick
Publikováno v:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
This work investigates the embeddings for representing dialog history in spoken language understanding (SLU) systems. We focus on the scenario when the semantic information is extracted directly from the speech signal by means of a single end-to-end
Externí odkaz:
http://arxiv.org/abs/2002.06012
Publikováno v:
Journal of Mechatronics, Electrical Power, and Vehicular Technology, Vol 13, Iss 1, Pp 48-59 (2022)
The study starts by modeling a simple 2-DOF (degrees of freedom) moving platform that employs two actuators to provide two kinds of rotational motion on the moving platform and each motion is driven by an electrical motor. A preliminary study to bett
Externí odkaz:
https://doaj.org/article/cd0ed841fe434461b884521eab49cebc
Continuous multimodal representations suitable for multimodal information retrieval are usually obtained with methods that heavily rely on multimodal autoencoders. In video hyperlinking, a task that aims at retrieving video segments, the state of the
Externí odkaz:
http://arxiv.org/abs/1705.05103
Autor:
Vukotić, Vedran, Pintea, Silvia-Laura, Raymond, Christian, Gravier, Guillaume, Van Gemert, Jan
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. In this work, we focus on anticipating future appearance given the current frame o
Externí odkaz:
http://arxiv.org/abs/1702.04125
Autor:
Nelwan, Olivia Syanne, Kalawat Lengkong, Victor Paskah, Mandey, Silvya Lefina, Saerang, Regina Trifena, Pratiknjo, Maria Heny, Manginsela, Elsje Pauline, Walangitan, Hengki Djemie, Ratag, Semuel Paulus, Paat, Frangky Jessy, Kawet, Raymond Christian
Publikováno v:
Environmental & Social Management Journal / Revista de Gestão Social e Ambiental; 2024, Vol. 18 Issue 6, p1-19, 19p