Browsing by Subject "Representations"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item “Fresh photos with the bold lighting” : visualizing the radical work of fat Black women on Instagram(2021-05-07) Spence, Christal Renee; Pinto, Samantha; Sebro, AdrienThrough close analysis of the Instagram profiles of the musician Lizzo and style influencer Kellie Brown, I will show how visibility can work as a strategy of innocuous resistance against hegemonic perceptions about fat black women. I historicize the work of fat black women on Instagram by examining representations of fat black women throughout history and in various forms of modern media, from Sarah Baartman to Kelli, Natasha Rothwell’s character from HBO’s Insecure. Lizzo and Kellie, through visibility, are defining themselves against dominant discourses around fat black femme embodiment and proclaiming the power and multifaceted personhood that fat black women possess. They are asserting themselves as full citizens under this white-patriarchal-hetero-capitalist system and are pushing back against boundaries about what it means to be a full citizen.Item Hostile relations : representing Arabs and Muslims in historically based war films(2017-04-27) Belcher, Samuel Ross; Wilkins, Karin Gwinn, 1962-; Atwood, Blake RThis thesis seeks to expand previous research on representations of Arabs and Muslims in Hollywood cinema by analyzing how recent historically based war films represent the aforementioned populations in their retelling of history. Drawing inspiration from Stuart Hall’s (1980) theory of encoding and decoding, as well as Marcia Landy’s (1996) writing on historically based film, this study inductively analyzes both the manner of retelling history and the encoding of Arabs and Muslims across multiple themes, namely: Space, Characterization, Violence, Language, and Civilians. In applying this lens to the films American Sniper (2014), 13 Hours (2016), Whiskey Tango Foxtrot (2016), and War Dogs (2016) a tendency to vilify, silence, and simplify Arabs and Muslims emerges. To provide context, the study utilizes work by scholars like Jack Shaheen (2001; 2008) and Evelyn Alsutany (2012) that previously documented representational methods for Arabs and Muslims. This thesis also places these films in conversation with the academic discourse on politics of fear and media framing to reveal a greater significance from their retelling of history, given the importance of politics of fear to the political decisions surrounding the historical context of each film. By reanimating these stories with generally negative and reductive representations of Arabs and Muslims, and asserting the importance and necessity of US military action, these films validate the politics of fear process, further entrenching the xenophobia attributed to Arabs and Muslims. While War Dogs challenges these ideas, at times, significant trends develop across the films to justify this reading.Item Learning methods for sequential decision making with imperfect representations(2011-12) Kalyanakrishnan, Shivaram 1983-; Stone, Peter, 1971-; Mooney, Raymond J; Miikkulainen, Risto; Ballard, Dana H; Parr, RonaldSequential decision making from experience, or reinforcement learning (RL), is a paradigm that is well-suited for agents seeking to optimize long-term gain as they carry out sensing, decision, and action in an unknown environment. RL tasks are commonly formulated as Markov Decision Problems (MDPs). Learning in finite MDPs enjoys several desirable properties, such as convergence, sample-efficiency, and the ability to realize optimal behavior. Key to achieving these properties is access to a perfect representation, under which the state and action sets of the MDP can be enumerated. Unfortunately, RL tasks encountered in the real world commonly suffer from state aliasing, and nearly always they demand generalization. As a consequence, learning in practice invariably amounts to learning with imperfect representations. In this dissertation, we examine the effect of imperfect representations on different classes of learning methods, and introduce techniques to improve their practical performance. We make four main contributions. First we introduce “parameterized learning problems”, a novel experimental methodology facilitating the systematic control of representational aspects such as state aliasing and generalization. Applying this methodology, we compare the class of on-line value function-based (VF) methods with the class of policy search (PS) methods. Results indicate clear patterns in the effects of representation on these classes of methods. Our second contribution is a deeper analysis of the limits imposed by representations on VF methods; specifically we provide a plausible explanation for the relatively poor performance of these methods on Tetris, the popular video game. The third major contribution of this dissertation is a formal study of the “subset selection” problem in multi-armed bandits. This problem, which directly affects the sample-efficiency of several commonly-used PS methods, also finds application in areas as diverse as industrial engineering and on-line advertising. We present new algorithms for subset selection and bound their performance under different evaluation criteria. Under a PAC setting, our sample complexity bounds indeed improve upon existing ones. As its fourth contribution, this dissertation introduces two hybrid learning architectures for combining the strengths of VF and PS methods. Under one architecture, these methods are applied in sequence; under the other, they are applied to separate components of a compound task. We demonstrate the effectiveness of these methods on a complex simulation of robot soccer. In sum, this dissertation makes philosophical, analytical, and methodological contributions towards the development of robust and automated learning methods for sequential decision making with imperfect representations