Sixth International Workshop on Languages for Modelling Variability (MODEVAR 2024)

• URL: http://arxiv.org/abs/2402.15511v1
• Date: Tue, 19 Dec 2023 08:28:06 GMT
• Title: Sixth International Workshop on Languages for Modelling Variability (MODEVAR 2024)
• Authors: Jessie Galasso-Carbonnel, Chico Sundermann,
• Abstract summary: This is the proceedings of the Sixth International Workshop on Languages for Modelling Variability (MODE 2024) which was held in Bern, Switzerland, February 06th 2024.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This is the proceedings of the Sixth International Workshop on Languages for Modelling Variability (MODEVAR 2024) which was held at Bern, Switzerland, February 06th 2024.

Related papers

• Proceedings Sixth International Workshop on Formal Methods for Autonomous Systems [0.5633692733812872]
  This volume contains the papers from the Sixth International Workshop on Formal Methods for Autonomous Systems (FMAS 2024) FMAS 2024 was co-located with 19th International Conference on integrated Formal Methods (iFM'24), hosted by the University of Manchester in the United Kingdom.
  arXiv  Detail & Related papers  (2024-11-20T11:21:22Z)
• Report on the 1st Workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) at SIGIR 2024 [37.103230004631996]
  The aim was to bring information retrieval researchers together around the topic of LLMs for evaluation in information retrieval. Given the novelty of the topic, the workshop was focused around multi-sided discussions.
  arXiv  Detail & Related papers  (2024-08-09T23:55:58Z)
• Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond [89.54151859266202]
  The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks.
  arXiv  Detail & Related papers  (2023-10-09T08:30:01Z)
• LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech [70.3307853082527]
  This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 hours of heterogeneous speech. It includes ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community.
  arXiv  Detail & Related papers  (2023-09-11T14:13:09Z)
• SwissBERT: The Multilingual Language Model for Switzerland [52.1701152610258]
  SwissBERT is a masked language model created specifically for processing Switzerland-related text. SwissBERT is a pre-trained model that we adapted to news articles written in the national languages of Switzerland. Since SwissBERT uses language adapters, it may be extended to Swiss German dialects in future work.
  arXiv  Detail & Related papers  (2023-03-23T14:44:47Z)
• Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges [0.8035384580801723]
  These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) on December 14th, 2021.
  arXiv  Detail & Related papers  (2023-01-10T14:53:28Z)
• ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition [96.87241233266448]
  This track was introduced in the Zero-Resource Speech challenge, 2021 edition, 2nd round. We motivate the new track and discuss participation rules in detail. We also present the two baseline systems that were developed for this track.
  arXiv  Detail & Related papers  (2021-07-14T08:29:07Z)
• Proceedings of the NeurIPS 2020 Workshop on Machine Learning for the Developing World: Improving Resilience [1.154829465058342]
  These are the proceedings of the 4th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS) on Saturday, December 12th 2020.
  arXiv  Detail & Related papers  (2021-01-12T08:35:54Z)
• A Discriminative Latent-Variable Model for Bilingual Lexicon Induction [100.76471407472599]
  We introduce a novel discriminative latent variable model for bilingual lexicon induction. Our model combines the bipartite matching dictionary prior of Haghighi et al. (2008) with a representation-based approach.
  arXiv  Detail & Related papers  (2018-08-28T14:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.