Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Armen Aghajanyan"'
Autor:
Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, Christoph Feichtenhofer
We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlappin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65db75a878a131a53042aa5e86727251
http://arxiv.org/abs/2109.14084
http://arxiv.org/abs/2109.14084
Publikováno v:
Separation Science and Technology. 55:771-778
The process of L-proline permeate desalting by electrodialysis with subsequent separation of L-proline from accompanying amino acids was developed. The overall yield of L-proline was 87.3%....
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb1ba65a60a63fd30b47e9b094e76c4f
https://doi.org/10.1080/01496395.2019.1574820
https://doi.org/10.1080/01496395.2019.1574820
Publikováno v:
NAACL-HLT
Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models. In spite of these advantages, widespread adoption of these models for real-time conversational use cases ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4e9b7368531b0facdfeb4f3939d3b9df
https://doi.org/10.18653/v1/2021.naacl-main.236
https://doi.org/10.18653/v1/2021.naacl-main.236
Autor:
Patrick Huber, Armen Aghajanyan, Barlas Oguz, Dmytro Okhonko, Scott Yih, Sonal Gupta, Xilun Chen
With the rise of large-scale pre-trained language models, open-domain question-answering (ODQA) has become an important research topic in NLP. Based on the popular pre-training fine-tuning approach, we posit that an additional in-domain pre-training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c810ba68365a8cab41f9c2ec8d790112
Autor:
Armen Aghajanyan, Michael Haeger, Michael Lewis, Akshat Shrivastava, Sonal Gupta, Keith Diedrick, Veselin Stoyanov, Haoran Li, Yashar Mehdad, Jean Maillard, Anuj Kumar
Publikováno v:
EMNLP (1)
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resoluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::222368bc321f43436d5948d0d4386437
http://arxiv.org/abs/2009.13655
http://arxiv.org/abs/2009.13655
Autor:
Matthew Hur, Armen Aghajanyan
Magnetic Resonance Imaging (MRI) provides three-dimensional anatomical and physiological details of the human brain. We describe the Integrated Voxel Analysis Method (IVAM) which, through machine learning, classifies MRI images of brains afflicted wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b49de49e308a6d6e0e00e353750cc8af
https://doi.org/10.1101/19009597
https://doi.org/10.1101/19009597
Publikováno v:
ACL (1)
When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language. However, current representa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c5f944c53642ca73504ac44847945666
https://doi.org/10.18653/v1/p19-1395
https://doi.org/10.18653/v1/p19-1395
Autor:
Armen Aghajanyan
Publikováno v:
CYBCONF
Deep neural networks are learning models with a very high capacity and therefore prone to over-fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the cap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c484bcce3e5b170f8f704244dbe6a2af