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- All Subjects: Machine Learning
- Creators: Computer Science and Engineering Program
- Resource Type: Text
In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
Designing these agents to cover every case of human interaction is difficult, and usually
imperfect, as human players are capable of learning to overcome these agents in unintended
ways. Artificial intelligence is a growing field that seeks to solve problems by simulating
learning in specific environments. The aim of this paper is to explore the applications that the
self play learning branch of artificial intelligence may pose on game development in the future,
and to attempt to implement a working version of a self play agent learning to play a Pokemon
battle. Originally designed Pokemon battle behavior is often suboptimal, getting stuck making
ineffective or incorrect choices, so training a self play model to learn the strategy and structure of
Pokemon battles from a clean slate would result in an organic agent that would outperform the
original behavior of the computer controlled agents. Though unsuccessful in my implementation,
this paper serves as a record of the exploration of this field, and a log of what worked and what
did not, in order to benefit any future person interested in the same topics.