8 July 2022
The NAVER LABS Europe Search and Recommendation team is presenting work on fairness in ranking and on sparse neural rankers such as SPLADE at this year’s SIGIR conference.
2022
16 June 2022
NAVER LABS Europe, together with colleagues from NAVER, LINE and WEBTOON will be in New Orleans. Visit the NAVER booth for job opportunities, internships, tech demos and more! 17 papers, workshop keynotes.
2022
25 February 2022
Multimodal pre-training has the potential of being a game changer in spoken language processing. In this blog, we review 3 recent papers on the topic published by Meta, Microsoft (and academic partners) and Google
2022
2 December 2021
Theoretical and experimental findings to improve regression applications: a 3D rotation case study. Code.
2021
29 November 2021
A novel, plug and play model for human 3D shape estimation of the body or hands, in videos which is trained by mimicking the BERT algorithm from the natural language processing community.
2021
4 November 2021
How to improve the inference speed of large Multilingual NTM models and do fast and parameter-efficient domain and language adaptation with them. Work published at EMNLP and WMT 2021.
2021
29 September 2021
A novel approach to indoor localization that uses magnetic field data from smartphone sensors and deep learning
2021
30 August 2021
A new approach to learning few-shot imitation agents whereby you simply feed demonstrations of a new test task to the learned policy called DCRL. This new approach has several advantages.
2021
3 August 2021
Podcast & edited transcript on Energy Based Models (EBMs). Guests, Hady Elsahar and Marc Dymetman work on EBMs in the field of natural language and co-organised the ICLR 2021 workshop on EBMs.
2021
15 July 2021
A new approach to learning few-shot imitation agents whereby you simply feed demonstrations of a new test task to the learned policy called DCRL. This new approach has several advantages.
2021
8 July 2021
A new sparse bi-encoder BERT-based model for effective and efficient first-stage ranking. The first to rival dense models.
2021
21 June 2021
Using domain randomization and meta-learning, computer vision models forget less when exposed to training samples from new domains. Remembering is a crucial element in the deployment of self-driving cars and robots which interact in dynamic environments.
2021