Zobrazeno 1 - 10
of 15 289
pro vyhledávání: '"A Dorr"'
Semantic role labeling (SRL) enriches many downstream applications, e.g., machine translation, question answering, summarization, and stance/belief detection. However, building multilingual SRL models is challenging due to the scarcity of semanticall
Externí odkaz:
http://arxiv.org/abs/2407.09283
Autor:
Mannekote, Amogh, Nam, Jinseok, Li, Ziming, Gao, Jian, Boyer, Kristy Elizabeth, Dorr, Bonnie J.
Indirect User Requests (IURs), such as "It's cold in here" instead of "Could you please increase the temperature?" are common in human-human task-oriented dialogue and require world knowledge and pragmatic reasoning from the listener. While large lan
Externí odkaz:
http://arxiv.org/abs/2406.07794
Autor:
Allen, Matthew J, Dorr, Francisco, Mejia, Joseph Alejandro Gallego, Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
Satellite-based remote sensing has revolutionised the way we address global challenges in a rapidly evolving world. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but processing these large datasets for use i
Externí odkaz:
http://arxiv.org/abs/2406.04230
Autor:
Liu, Zoey, Dorr, Bonnie J.
Recent work to enhance data partitioning strategies for more realistic model evaluation face challenges in providing a clear optimal choice. This study addresses these challenges, focusing on morphological segmentation and synthesizing limitations re
Externí odkaz:
http://arxiv.org/abs/2404.09371
Task-oriented dialogue systems are expected to handle a constantly expanding set of intents and domains even after they have been deployed to support more and more functionalities. To live up to this expectation, it becomes critical to mitigate the c
Externí odkaz:
http://arxiv.org/abs/2402.14155
Autor:
Kazakova, Vera A., Hwang, Jena D., Dorr, Bonnie J., Wilks, Yorick, Gage, J. Blake, Memory, Alex, Clark, Mark A.
Publikováno v:
FLAIRS-2019
Effective cyber threat recognition and prevention demand comprehensible forecasting systems, as prior approaches commonly offer limited and, ultimately, unconvincing information. We introduce Simplified Plaintext Language (SPLAIN), a natural language
Externí odkaz:
http://arxiv.org/abs/2311.11215
Autor:
Gallego-Mejia, Joseph A., Jungbluth, Anna, Martínez-Ferrer, Laura, Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, Ramos-Pollán, Raúl
Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm a
Externí odkaz:
http://arxiv.org/abs/2310.03513
Autor:
Martínez-Ferrer, Laura, Jungbluth, Anna, Gallego-Mejia, Joseph A., Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, Ramos-Pollán, Raúl
In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe). We fine-tune the models on smaller labeled datasets to predict vegetation percentage, and empir
Externí odkaz:
http://arxiv.org/abs/2310.02048
Autor:
Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making
Externí odkaz:
http://arxiv.org/abs/2310.00826
Autor:
Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, Ramos-Pollán, Raúl
In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and interferom
Externí odkaz:
http://arxiv.org/abs/2310.00119