Courant Master'S Thesis

We propose two new methods: (1) Ada Boost Inspired Mini- Batch Sampling (ABIMBS) and (2) Ada Boost Inspired Sentence Sampling (ABISS) ensemble methods for the language modeling task.ABIMBS has a forward and backward variant and ABISS has a standard deviation and square root variant.

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Thus, we solve a fundamental algorithmic question in the theory of multivariate polynomials over the integers.

As a direct consequence, we also obtain a solution to certain foundational problem intrinsic to Kronecker's programme for constructive mathematics and provide an effective version of Hilbert's basis theorem.

This networks are then model averaged using standard Bayesian model averaging and used for prediction.

[Find Out More] In this project, ICU 30-day readmission was predicted using a multivariate panel of physiological measurements.

[Find Out More] In this project, the impact turnovers and turnover differential has on winning and scoring in the NBA were identified.

NBA regular season data from basketball-reference were used.My current research focus is on applications of distributed systems, deep learning, and computer vision approaches to automated reality modeling.Highlights of my graduate work included the Cell and Pilaf projects exploring RDMA-based distributed systems, as well as Oolong.I am a Research Scientist at the AI Foundation, where I work on generation and detection of speech and text from a machine learning perspective. I’m a huge supporter of Borussia Dortmund from the German Bundesliga.Previously, I completed my MS in Computer Science at the Courant Institute at NYU in the CILVR group focusing on deep learning applied to natural language processing and advised by Kyunghyun Cho and Sam Bowman. I dabble in using machine learning and statistics for sports analytics.I defended my Ph D thesis in Computer Science, focusing on Distributed Systems, in August 2015: "Building Fast, CPU-Efficient Distributed Systems on Ultra-Low Latency, RDMA-Capable Networks".I earned my Master's degree in Electrical Engineering in May 2010 and my BE in EE in May 2009 from the Cooper Union in New York City.We aim to leverage Conv Nets and use pretrained meta-features from st ate-of-the-art image classification architectures.[Find Out More] This project developed a pipeline that uses TCGA gene expression and molecular data as features and cellularity as the outcome variable to learn structures of Bayesian networks using max-min hill climbing.Project in which alternative methodologies to improve state-of-the-art language model performance with LSTMs are being developed.[Find Out More] This project aims to predict data-driven head-neck cancer subtypes using pathology images.

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