Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Mahmud, Jalal"'
Modern GPUs are designed for regular problems and suffer from load imbalance when processing irregular data. Prior to our work, a domain expert selects the best kernel to map fine-grained irregular parallelism to a GPU. We instead propose Seer, an ab
Externí odkaz:
http://arxiv.org/abs/2403.17017
When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed
Externí odkaz:
http://arxiv.org/abs/2106.00954
Customers of machine learning systems demand accountability from the companies employing these algorithms for various prediction tasks. Accountability requires understanding of system limit and condition of erroneous predictions, as customers are oft
Externí odkaz:
http://arxiv.org/abs/2105.04707
Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data. Traditional tri-training algorithm and tri-training with disagreement have shown promise
Externí odkaz:
http://arxiv.org/abs/1909.11233
Publikováno v:
Proceedings of the 10th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2019
Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of negation in customer service interactions, particularly applied to sentiment analysis. We
Externí odkaz:
http://arxiv.org/abs/1906.04706
Autor:
Akkiraju, Rama, Sinha, Vibha, Xu, Anbang, Mahmud, Jalal, Gundecha, Pritam, Liu, Zhe, Liu, Xiaotong, Schumacher, John
Academic literature on machine learning modeling fails to address how to make machine learning models work for enterprises. For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to
Externí odkaz:
http://arxiv.org/abs/1811.04871
In the last several years, Twitter is being adopted by the companies as an alternative platform to interact with the customers to address their concerns. With the abundance of such unconventional conversation resources, push for developing effective
Externí odkaz:
http://arxiv.org/abs/1807.06107
Users' persistent social media contents like posts on Facebook Timeline are presented as an "exhibition" about the person to others, and managing these exhibitional contents for impression management needs intentional and manual efforts. To raise awa
Externí odkaz:
http://arxiv.org/abs/1710.04205
Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we deve
Externí odkaz:
http://arxiv.org/abs/1709.05413
Autor:
Arnoux, Pierre-Hadrien, Xu, Anbang, Boyette, Neil, Mahmud, Jalal, Akkiraju, Rama, Sinha, Vibha
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we
Externí odkaz:
http://arxiv.org/abs/1704.05513