Computer Sciences Commons^™

A Symbolic Music Transformer For Real-Time Expressive Performance And Improvisation, Arnav Shirodkar

Senior Projects Fall 2023

With the widespread proliferation of AI technology, deep architectures — many of which are based on neural networks — have been incredibly successful in a variety of different research areas and applications. Within the relatively new domain of Music Information Retrieval (MIR), deep neural networks have also been successful for a variety of tasks, including tempo estimation, beat detection, genre classification, and more. Drawing inspiration from projects like George E. Lewis's Voyager and Al Biles's GenJam, two pioneering endeavors in human-computer interaction, this project attempts to tackle the problem of expressive music generation and seeks to create a Symbolic Music …

Relevance-Tcav: Explaining Deep Neural Nets In Human Concepts, Henning Fischel

Senior Projects Spring 2021

Neural Networks, a form of machine learning, are used in increasingly important roles in the modern world. They are being used in self-driving cars and medical diagnoses. However, they are “Black Boxes”: they cannot be easily interpreted by humans. This project combines two methods of explaining a neural network’s decisions in an attempt to improve their accuracy. This new method, relevance-based testing with concept activation vectors (R-TCAV), yields promising results on two small experiments but is less precise than the previous TCAV method.

Don't Take This Personally: Sentiment Analysis For Identification Of "Subtweeting" On Twitter, Noah L. Segal-Gould

Senior Projects Spring 2018

The purpose of this project is to identify subtweets. The Oxford English Dictionary defines "subtweet" as a "[Twitter post] that refers to a particular user without directly mentioning them, typically as a form of furtive mockery or criticism." This paper details a process for gathering a labeled ground truth dataset, training a classifier, and creating a Twitter bot which interacts with subtweets in real time. The Naive Bayes classifier trained in this project classifies tweets as subtweets and non-subtweets with an average F1 score of 72%.

A Study Of Neural Networks For The Quantum Many-Body Problem, Liam B. Schramm

Senior Projects Spring 2018

One of the fundamental problems in analytically approaching the quantum many-body problem is that the amount of information needed to describe a quantum state. As the number of particles in a system grows, the amount of information needed for a full description of the system increases exponentially. A great deal of work then has gone into finding efficient approximate representations of these systems. Among the most popular techniques are Tensor Networks and Quantum Monte Carlo methods. However, one new method with a number of promising theoretical guarantees is the Neural Quantum State. This method is an adaptation of the Restricted …

Radical Recognition In Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization, Xiangying Shuai

Senior Projects Spring 2016

In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics.

Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific …