Zobrazeno 1 - 10
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pro vyhledávání: '"Eva, L."'
Autor:
Schiewe Jürgen
Publikováno v:
Zeitschrift für Rezensionen zur Germanistischen Sprachwissenschaft, Vol 13, Iss 1-2, Pp 117-124 (2021)
Externí odkaz:
https://doaj.org/article/455e36d01cff4be48ca39d0c9071cd5e
Autor:
Liu, Ran, Ma, Wenrui, Zippi, Ellen, Pouransari, Hadi, Xiao, Jingyun, Sandino, Chris, Mahasseni, Behrooz, Minxha, Juri, Azemi, Erdrin, Dyer, Eva L., Moin, Ali
Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their functional properties. In this work, we propose a novel objective for trans
Externí odkaz:
http://arxiv.org/abs/2410.08421
Autor:
Kaushik, Chiraag, Liu, Ran, Lin, Chi-Heng, Khera, Amrit, Jin, Matthew Y, Ma, Wenrui, Muthukumar, Vidya, Dyer, Eva L
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance. This issue of class bias is widely studied in cases of datasets with sample imbalance, but is relative
Externí odkaz:
http://arxiv.org/abs/2402.11742
Autor:
Azabou, Mehdi, Arora, Vinam, Ganesh, Venkataramana, Mao, Ximeng, Nachimuthu, Santosh, Mendelson, Michael J., Richards, Blake, Perich, Matthew G., Lajoie, Guillaume, Dyer, Eva L.
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and datasets. However, the integration of many neural recordings into one unified model is challenging, as ea
Externí odkaz:
http://arxiv.org/abs/2310.16046
Autor:
Liu, Ran, Khose, Sahil, Xiao, Jingyun, Sathidevi, Lakshmi, Ramnath, Keerthan, Kira, Zsolt, Dyer, Eva L.
Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge, we propose a novel approach for distribution-aware latent augmentatio
Externí odkaz:
http://arxiv.org/abs/2308.14596
Autor:
Azabou, Mehdi, Ganesh, Venkataramana, Thakoor, Shantanu, Lin, Chi-Heng, Sathidevi, Lakshmi, Liu, Ran, Valko, Michal, Veličković, Petar, Dyer, Eva L.
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we intr
Externí odkaz:
http://arxiv.org/abs/2308.09198
Autor:
Nadav L. Sprague, Isabel B. Fan, Michelle Dandeneau, Jorge Fabian Hernandez Perez, Jordyn Birmingham, Daritza De Los Santos, Milan I. Riddick, Gabriella Y. Meltzer, Eva L. Siegel, Diana Hernández
Publikováno v:
Humanities & Social Sciences Communications, Vol 11, Iss 1, Pp 1-15 (2024)
Abstract This study introduces StreetTalk, an original qualitative research methodology inspired by social media influencers, to investigate perceptions and experiences of energy insecurity among New York City (NYC) residents. Briefly, energy insecur
Externí odkaz:
https://doaj.org/article/6b948e010200400fb59029375258a121
Autor:
Jürgen Schiewe
Publikováno v:
Zeitschrift für Rezensionen zur Germanistischen Sprachwissenschaft, Vol 13, Iss 1-2, Pp 117-124 (2021)