Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Doran, Derek"'
Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input drive the decisions of a DNN. In countless real-world domains, from leg
Externí odkaz:
http://arxiv.org/abs/2004.14545
Autor:
Zabihimayvan, Mahdieh, Doran, Derek
Tor is one of the most well-known networks that protects the identity of both content providers and their clients against any tracking or tracing on the Internet. So far, most research attention has been focused on investigating the security and priv
Externí odkaz:
http://arxiv.org/abs/1911.07814
In this paper, we introduce a contextual grounding approach that captures the context in corresponding text entities and image regions to improve the grounding accuracy. Specifically, the proposed architecture accepts pre-trained text token embedding
Externí odkaz:
http://arxiv.org/abs/1911.02133
Autor:
Zabihimayvan, Mahdieh, Doran, Derek
Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to det
Externí odkaz:
http://arxiv.org/abs/1903.05675
Tor is among most well-known dark net in the world. It has noble uses, including as a platform for free speech and information dissemination under the guise of true anonymity, but may be culturally better known as a conduit for criminal activity and
Externí odkaz:
http://arxiv.org/abs/1902.06680
Existing visual reasoning datasets such as Visual Question Answering (VQA), often suffer from biases conditioned on the question, image or answer distributions. The recently proposed CLEVR dataset addresses these limitations and requires fine-grained
Externí odkaz:
http://arxiv.org/abs/1901.06706
Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained. This precludes the use of powerful supervised techniques such as neural networks. In this study we propose a new unsupervised and sem
Externí odkaz:
http://arxiv.org/abs/1811.10658
We introduce a new inference task - Visual Entailment (VE) - which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a natural language sentence as in TE tasks. A novel dataset SNLI-VE (publi
Externí odkaz:
http://arxiv.org/abs/1811.10582
Autor:
Ebrahimi, Monireh, Sarker, Md Kamruzzaman, Bianchi, Federico, Xie, Ning, Doran, Derek, Hitzler, Pascal
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from know
Externí odkaz:
http://arxiv.org/abs/1811.04132