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Full-Text Articles in Physical Sciences and Mathematics
Stereotypes And Language Models: Understanding How Language Models Encode Stereotypes, Debiasing Language Models, And Examining How Stereotypes Affect Conversations, Brian C. Wang
Computer Science Senior Theses
This thesis describes a variety of approaches in examining how language models encode stereotypes (understanding stereotypes from a model point-of-view), debiasing language models, and using language models to understand how stereotypes affect conversations (understanding stereotypes from a conversational point-of-view). We present a novel approach for textual clues analysis that makes language models more interpretable, combining the understanding of what stereotypes the internal structures of language models have encoded during their initial training (via attention-based analysis) and understanding what textual clues are most relevant to identifying stereotypes for models trained to detect stereotypes (via SHAP-based analysis). We find that different pre-trained …
An Algorithmic Approach To Jazz Guitar Voice-Leading Chord Fingerings, Matthew B. Keating
An Algorithmic Approach To Jazz Guitar Voice-Leading Chord Fingerings, Matthew B. Keating
Computer Science Senior Theses
A problem in guitar practice is choosing chord voicings that fit together in sequence, a process known as voice leading. In jazz, a guitarist follows voice leading by maintaining stepwise or limited motion for smoother harmony. The main avenues to learn jazz guitar voice leading theory are through a guitar instructor or chord books. To our knowledge, no computational method of generating voice-leading given chord labels exists. First, we demonstrate the complexity of this problem by presenting a graph search algorithm to optimize for a simplified version of voice leading. Then, we present a novel approach to algorithmically derive tablature …
Symplectically Integrated Symbolic Regression Of Hamiltonian Dynamical Systems, Daniel Dipietro
Symplectically Integrated Symbolic Regression Of Hamiltonian Dynamical Systems, Daniel Dipietro
Computer Science Senior Theses
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data. SISR employs a deep symbolic regression approach, using a multi-layer LSTMRNN with mutation to probabilistically sample Hamiltonian symbolic expressions. Using symplectic neural networks, we develop a model-agnostic approach for extracting meaningful physical priors from the data that can be imposed on-the-fly into the RNN output, limiting its search space. Hamiltonians generated by the RNN are optimized and assessed using a fourth-order symplectic integration scheme; prediction performance is used to train the LSTM-RNN to generate increasingly better functions via a risk-seeking policy gradients …
Entity Based Sentiment Analysis For Textual Health Advice, Dae Lim Chung
Entity Based Sentiment Analysis For Textual Health Advice, Dae Lim Chung
Computer Science Senior Theses
This work explores entity based sentiment analysis for textual health advice through deep learning. We fine tuned a pretrained BERT model to analyze sentiments across five different predetermined categories which consist of food, medicine, disease, exercise, and vitality for three different sentiments: positive, negative, and neutral. Original set of annotated medical dataset from Dartmouth College’s Persist Lab was used to conduct the experiments. For the aim of tailoring the data for the purpose of entity based sentiment analysis, we explored data transformation techniques to generate optimum training examples. During the experiments, we were able to discover that the wide variety …
Analyzing Behavioral Adaptation To Covid-19 And Return To Pre-Pandemic Baselines In A Cohort Of College Seniors, Vlado Vojdanovski
Analyzing Behavioral Adaptation To Covid-19 And Return To Pre-Pandemic Baselines In A Cohort Of College Seniors, Vlado Vojdanovski
Computer Science Senior Theses
As the critical phase of the COVID-19 pandemic seems to be winding down, it is important to analyze the adjustment to COVID-19 and return to normalcy of various populations. In this study we focus on the behavioral adjustments exhibited by a cohort of N=114 college seniors. To infer COVID-19 adjustment we compare the 2021 year (second year of COVID-19) to the 2020 year (first year of COVID-19) and 2019 (prepandemic baseline year). We begin with a broad analysis between the second and first covid year, finding that the second year of COVID-19 shows significant returns to pre-pandemic baselines on multiple …