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Wojciech Stokowiec

dblp:184/8854 · DBLP profile ↗
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6ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0002-8076-4750ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Language models and text generation · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › decoding
adaptive decoding
0.612022
Enabling Arbitrary Translation Objectives with Adaptive Tree Search · ICLR 2022
Natural language and speech › Language models and text generation
decoding
0.612022
Enabling Arbitrary Translation Objectives with Adaptive Tree Search · ICLR 2022
Natural language and speech › Language models and text generation
text generation
0.612022
Enabling Arbitrary Translation Objectives with Adaptive Tree Search · ICLR 2022

Methods — techniques the papers use, named apart from their topics

tree search · 0.6
YearPublicationVenuePosition
2022 Enabling Arbitrary Translation Objectives with Adaptive Tree Search
Wang Ling, Wojciech Stokowiec, Domenic Donato, Chris Dyer, Lei Yu 0008, Laurent Sartran, Austin Matthews
ICLR2
2020 Adversarial autoencoders for compact representations of 3D point clouds
Maciej Zamorski, Maciej Zieba, Piotr Klukowski, Rafal Nowak, Karol Kurach, Wojciech Stokowiec, Tomasz Trzcinski
Comput. Vis. Image Underst.6
2020 Better Document-Level Machine Translation with Bayes' Rule
abstract
We show that Bayes’ rule provides an effective mechanism for creating document translation models that can be learned from only parallel sentences and monolingual documents a compelling benefit because parallel documents are not always available. In our formulation, the posterior probability of a candidate translation is the product of the unconditional (prior) probability of the candidate output document and the “reverse translation probability” of translating the candidate output back into the source language. Our proposed model uses a powerful autoregressive language model as the prior on target language documents, but it assumes that each sentence is translated independently from the target to the source language. Crucially, at test time, when a source document is observed, the document language model prior induces dependencies between the translations of the source sentences in the posterior. The model’s independence assumption not only enables efficient use of available data, but it additionally admits a practical left-to-right beam-search algorithm for carrying out inference. Experiments show that our model benefits from using cross-sentence context in the language model, and it outperforms existing document translation approaches.
Lei Yu 0008, Laurent Sartran, Wojciech Stokowiec, Wang Ling, Lingpeng Kong, Phil Blunsom, Chris Dyer
Trans. Assoc. Comput. Linguistics3
2017 What Looks Good with my Sofa: Ensemble Multimodal Search for Interior Design
abstract
In this paper, we propose a multi-modal search engine for interior design that combines visual and textual queries.The goal of our engine is to retrieve interior objects, e.g.furniture or wall clocks, that share visual and aesthetic similarities with the query.Our search engine allows the user to take a photo of a room and retrieve with a high recall a list of items identical or visually similar to those present in the photo.Additionally, it allows to return other items that aesthetically and stylistically fit well together.To achieve this goal, our system blends the results obtained using textual and visual modalities.Thanks to this blending strategy, we increase the average style similarity score of the retrieved items by 11%.Our work is implemented as a Web-based application and it is planned to be opened to the public.
Ivona Tautkute, Aleksandra Mozejko, Wojciech Stokowiec, Tomasz Trzcinski, Lukasz Brocki, Krzysztof Marasek
FedCSIS3
2017 Shallow Reading with Deep Learning: Predicting Popularity of Online Content Using only Its Title
Wojciech Stokowiec, Tomasz Trzcinski, Krzysztof Wolk, Krzysztof Marasek, Przemyslaw Rokita
ISMIS1
2016 LanguageCrawl: A Generic Tool for Building Language Models Upon Common-Crawl
Szymon Roziewski, Wojciech Stokowiec
LREC2