Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Ziko P"'
Autor:
Nicolas, Julien, Chiaroni, Florent, Ziko, Imtiaz, Ahmad, Ola, Desrosiers, Christian, Dolz, Jose
Despite the recent progress in incremental learning, addressing catastrophic forgetting under distributional drift is still an open and important problem. Indeed, while state-of-the-art domain incremental learning (DIL) methods perform satisfactorily
Externí odkaz:
http://arxiv.org/abs/2307.05707
We introduce a simple non-linear embedding adaptation layer, which is fine-tuned on top of fixed pre-trained features for one-shot tasks, improving significantly transductive entropy-based inference for low-shot regimes. Our norm-induced transformati
Externí odkaz:
http://arxiv.org/abs/2304.06832
We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem. Specifically, we propose a bi-level optimization formulation, which explores a parameterized family of objective functions, each eval
Externí odkaz:
http://arxiv.org/abs/2212.00334
Autor:
Robert Hambwalula, Mary Kagujje, Innocent Mwaba, Dennis Musonda, David Singini, Lilungwe Mutti, Nsala Sanjase, Paul C. Kaumba, Luunga M. Ziko, Kevin M. Zimba, Pauline Kasese-Chanda, Monde Muyoyeta
Publikováno v:
BMC Public Health, Vol 24, Iss 1, Pp 1-8 (2024)
Abstract Background Globally, at least 3 million TB patients are missed every year. In Zambia, the TB treatment coverage increased from 66% in 2020 to 92% in 2022. Involvement of all levels of health care service delivery is critical to finding all t
Externí odkaz:
https://doaj.org/article/0603962f14574d5f8d6c059340ed0fc5
Autor:
Boudiaf, Malik, Masud, Ziko Imtiaz, Rony, Jérôme, Dolz, Jose, Ayed, Ismail Ben, Piantanida, Pablo
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based
Externí odkaz:
http://arxiv.org/abs/2106.12252
We investigate a general formulation for clustering and transductive few-shot learning, which integrates prototype-based objectives, Laplacian regularization and supervision constraints from a few labeled data points. We propose a concave-convex rela
Externí odkaz:
http://arxiv.org/abs/2106.09516
Autor:
Mihaela Deaconu, Anil Abduraman, Ana-Maria Brezoiu, Nada K. Sedky, Simona Ioniță, Cristian Matei, Laila Ziko, Daniela Berger
Publikováno v:
Molecules, Vol 29, Iss 13, p 3122 (2024)
This study presents properties of hydroethanolic extracts prepared from Pinot Noir (PN) grape pomace through conventional, ultrasound-assisted or solvothermal extraction. The components of the extracts were identified by HPLC. The total content of po
Externí odkaz:
https://doaj.org/article/3a431720a98646a9b4b7068a38b18408
Autor:
Boudiaf, Malik, Kervadec, Hoel, Masud, Ziko Imtiaz, Piantanida, Pablo, Ayed, Ismail Ben, Dolz, Jose
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a gi
Externí odkaz:
http://arxiv.org/abs/2012.06166
Autor:
Boudiaf, Malik, Masud, Ziko Imtiaz, Rony, Jérôme, Dolz, José, Piantanida, Pablo, Ayed, Ismail Ben
We introduce Transductive Infomation Maximization (TIM) for few-shot learning. Our method maximizes the mutual information between the query features and their label predictions for a given few-shot task, in conjunction with a supervision loss based
Externí odkaz:
http://arxiv.org/abs/2008.11297
We propose a transductive Laplacian-regularized inference for few-shot tasks. Given any feature embedding learned from the base classes, we minimize a quadratic binary-assignment function containing two terms: (1) a unary term assigning query samples
Externí odkaz:
http://arxiv.org/abs/2006.15486