no code implementations • COLING (WANLP) 2020 • Ryan Muther, David Smith
We present our work on automatically detecting isnads, the chains of authorities for a re-port that serve as citations in hadith and other classical Arabic texts.
no code implementations • 15 Apr 2024 • Mengmeng Yang, Ming Ding, Youyang Qu, Wei Ni, David Smith, Thierry Rakotoarivelo
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
no code implementations • 29 Jun 2023 • Youyang Qu, Lichuan Ma, Wenjie Ye, Xuemeng Zhai, Shui Yu, Yunfeng Li, David Smith
Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining.
no code implementations • 29 Jun 2023 • Ryan Muther, David Smith
This paper explores new methods for locating the sources used to write a text, by fine-tuning a variety of language models to rerank candidate sources.
no code implementations • 12 May 2023 • Youyang Qu, Xin Yuan, Ming Ding, Wei Ni, Thierry Rakotoarivelo, David Smith
This inspired recent research on removing the influence of specific data samples from a trained ML model.
no code implementations • 7 Apr 2023 • Yuning Xing, Dexter Pham, Henry Williams, David Smith, Ho Seok Ahn, JongYoon Lim, Bruce A. MacDonald, Mahla Nejati
The overall measurement system (leaf detection and size estimation algorithms combine) delivers an RMSE value of 8. 13mm and an R^2 value of 0. 899.
no code implementations • 20 Feb 2023 • Ans Qureshi, Neville Loh, Young Min Kwon, David Smith, Trevor Gee, Oliver Bachelor, Josh McCulloch, Mahla Nejati, JongYoon Lim, Richard Green, Ho Seok Ahn, Bruce MacDonald, Henry Williams
Following a global trend, the lack of reliable access to skilled labour is causing critical issues for the effective management of apple orchards.
no code implementations • 31 Aug 2022 • Ryan Muther, David Smith
We examine the tradeoffs in model performance involved in choices of training sample and filter training and test data in heavily imbalanced token classification task and examine the relationship between the magnitude of these tradeoffs and the base rate of the phenomenon of interest.
no code implementations • 31 Aug 2022 • Ryan Muther, David Smith
Unlike prior work, therefore, we seek to leverage the information that can be gained from looking at association networks of individuals derived from textual evidence in order to disambiguate names.
no code implementations • EACL 2021 • Ansel MacLaughlin, David Smith
We explore the task of quotability identification, in which, given a document, we aim to identify which of its passages are the most quotable, i. e. the most likely to be directly quoted by later derived documents.
no code implementations • EACL 2021 • Rui Dong, David Smith
Starting from the Table Parsing (TAPAS) model developed for question answering (Herzig et al., 2020), we find that modeling table structure improves a language model pre-trained on unstructured text.
1 code implementation • 25 Mar 2021 • David Smith, Frederik Geth, Elliott Vercoe, Andrew Feutrill, Ming Ding, Jonathan Chan, James Foster, Thierry Rakotoarivelo
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.
no code implementations • COLING 2020 • Shijia Liu, David Smith
Code-switching has long interested linguists, with computational work in particular focusing on speech and social media data (Sitaram et al., 2019).
Optical Character Recognition Optical Character Recognition (OCR)
no code implementations • 24 Nov 2020 • Sachin Grover, David Smith, Subbarao Kambhampati
We show how to generate questions to refine the robot's understanding of the teammate's model.
no code implementations • 2 Jul 2020 • Anagha Kulkarni, Sarath Sreedharan, Sarah Keren, Tathagata Chakraborti, David Smith, Subbarao Kambhampati
Given structured environments (like warehouses and restaurants), it may be possible to design the environment so as to boost the interpretability of the robot's behavior or to shape the human's expectations of the robot's behavior.
no code implementations • LREC 2020 • Maha Alkhairy, Afshan Jafri, David Smith
Accuracy results are: root computed from a word (92{\%}), word generation from a root (100{\%}), non-root properties of a word (97{\%}), and diacritization (84{\%}).
no code implementations • 18 Mar 2020 • Farhad Farokhi, Nan Wu, David Smith, Mohamed Ali Kaafar
The experiments illustrate that collaboration among more than 10 data owners with at least 10, 000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy.
no code implementations • 20 Dec 2019 • Laura Dietz, Bhaskar Mitra, Jeremy Pickens, Hana Anber, Sandeep Avula, Asia Biega, Adrian Boteanu, Shubham Chatterjee, Jeff Dalton, Shiri Dori-Hacohen, John Foley, Henry Feild, Ben Gamari, Rosie Jones, Pallika Kanani, Sumanta Kashyapi, Widad Machmouchi, Matthew Mitsui, Steve Nole, Alexandre Tachard Passos, Jordan Ramsdell, Adam Roegiest, David Smith, Alessandro Sordoni
The vision of HIPstIR is that early stage information retrieval (IR) researchers get together to develop a future for non-mainstream ideas and research agendas in IR.
no code implementations • ICCV 2019 • David Smith, Matthew Loper, Xiaochen Hu, Paris Mavroidis, Javier Romero
Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision.
no code implementations • 14 Aug 2019 • Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele Magazzeni, David Smith
Explainable AI is an important area of research within which Explainable Planning is an emerging topic.
no code implementations • 24 Jun 2019 • Nan Wu, Farhad Farokhi, David Smith, Mohamed Ali Kaafar
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets.
no code implementations • WS 2019 • Rui Dong, David Smith, Shiran Dudy, Steven Bedrick
Language models have broad adoption in predictive typing tasks.
no code implementations • 19 Mar 2019 • Sarath Sreedharan, Siddharth Srivastava, David Smith, Subbarao Kambhampati
Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans.
no code implementations • ACL 2018 • Rui Dong, David Smith
We propose a novel approach to OCR post-correction that exploits repeated texts in large corpora both as a source of noisy target outputs for unsupervised training and as a source of evidence when decoding.
no code implementations • WS 2018 • Shiran Dudy, Shaobin Xu, Steven Bedrick, David Smith
Brain-computer interfaces and other augmentative and alternative communication devices introduce language-modeing challenges distinct from other character-entry methods.
no code implementations • 30 Jun 2016 • David Smith, Parag Singla, Vibhav Gogate
Due to the intractable nature of exact lifted inference, research has recently focused on the discovery of accurate and efficient approximate inference algorithms in Statistical Relational Models (SRMs), such as Lifted First-Order Belief Propagation.