Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Avishek Anand"'
Publikováno v:
Applied Network Science, Vol 4, Iss 1, Pp 1-22 (2019)
Abstract Most real-world graphs collected from the Web like Web graphs and social network graphs are partially discovered or crawled. This leads to inaccurate estimates of graph properties based on link analysis such as PageRank. In this paper we foc
Externí odkaz:
https://doaj.org/article/f199358282c145ce8d0bf736882d7023
Publikováno v:
ACM Transactions on Information Systems. 41:1-31
Neural document ranking models perform impressively well due to superior language understanding gained from pre-training tasks. However, due to their complexity and large number of parameters, these (typically transformer-based) models are often non-
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031282379
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24d7ecbbd7e48882b6e44b80a90a1ffa
https://doi.org/10.1007/978-3-031-28238-6_17
https://doi.org/10.1007/978-3-031-28238-6_17
Autor:
Lijun Lyu, Avishek Anand
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031282430
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::92d5778a447437e36f603329b2d08c6e
https://doi.org/10.1007/978-3-031-28244-7_41
https://doi.org/10.1007/978-3-031-28244-7_41
Autor:
Avishek Anand, Rishiraj Saha Roy
Publikováno v:
Synthesis Lectures on Information Concepts, Retrieval, and Services. 13:1-194
Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a mul...
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f25fedfa5fbd945502a128abe5e8667
http://arxiv.org/abs/2207.03153
http://arxiv.org/abs/2207.03153
Publikováno v:
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
We introduce SparcAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated bas
Publikováno v:
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 8:43-52
Recent advances in machine learning have led to the widespread adoption of ML models for decision support systems. However, little is known about how the introduction of such systems affects the behavior of human stakeholders. This pertains both to t
Publikováno v:
ACM SIGIR Forum. 54:1-11
In the week of November 10--15, 2019, 44 researchers from the fields of information retrieval and Web search, natural language processing, human computer interaction, and dialogue systems met for the Dagstuhl Seminar 19461 "Conversational Search" to
Autor:
Rishiraj Saha Roy, Avishek Anand
Publikováno v:
Synthesis Lectures on Information Concepts, Retrieval, and Services ISBN: 9783031795114
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a639230d734af22ccd7a07fef8ac14f
https://doi.org/10.1007/978-3-031-79512-1_4
https://doi.org/10.1007/978-3-031-79512-1_4