Literature

By Joana Breidenbach. 1994

Colonized Australia presents a very grim picture of Aboriginal people in general. They are in a minority in their own land and many Aboriginal trib es and languages in fact, became extinct. Land, sacred to Aboriginals, has been lost to the c olonizers. It is only the Aboriginal consciousness that keeps them united in this predic ament in spite of their differences in language, culture, colour, region and religion. It is also this unity that makes them fight, at times with a martial spirit, against discrimination and m otivates them to assert their Aboriginal identity. In the past few decades Australia has pro duced a considerable amount of Aboriginal Literature reflecting Aboriginal struggle economic freedom, legal recognition and reforms for basic living conditions. Mudrooroo, Jack Davis, Ale xis Wright, Kim Scott, and other Aboriginal Writers represent these issues through different li terary genres of poetry, fiction and drama. In this paper I try to explain how Aboriginal form can be identified and how functions of Aboriginality can be recognized in the writings of Mudrooroo and Jack Davis writings that belong to the early phase of Aboriginal period.

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Citations in the corpus

Learning Algorithms for Active Learning (2017, background): “Copyright 2017 by the author(s). ing the instance whose label the model is most uncertain about, or the instance whose label is expected to maximally reduce the model’s uncertainty about labels for other instances (Gilad-Bachrach et al., 2005; Settles, 2010; Houlsby et al., 2011).”
Adversarial Active Learning for Deep Networks: a Margin Based Approach (2018, methodology): “For a review of classic active learning methods and their applications, we refer the reader to Burr Settles (Settles, 2010).”
Cleaning Crowdsourced Labels Using Oracles For Statistical Classification (2018, background): “One straight-forward solution is to apply active cleaning [36] (e.g., uncertain sampling) to determine which label should be cleaned.”
An Overview of Deep Semi-Supervised Learning (2020, background): “Although it is not possible to obtain a universally good active learning strategy [33], there exist many heuristics [140], which have been proven to be eﬀective in practice.”
Online Multi-Task Learning Using Active Sampling (2017, methodology): “The core hypothesis which drives active learning in the usual machine learning contexts (such as classiﬁcation problems) is that a machine learning algorithm can achieve better performance with fewer labeled training examples if it is allowed to choose the data from which it learns (Settles, 2010).”
Interactive Multi-task Relationship Learning (2016, background): “The pool based active learning approaches select the most informative unlabeled instance iteratively, which is then labeled by user, with the goal of learning a better model with less efforts [26].”