In 2013, I graduated from the University of Cambridge in Information and Computer Engineering (BA, MEng). Following this, I was with a start-up for two years, working on the research and development of speech recognition software.
My current research interests are primarily in machine learning methods motivated by clinical medicine, spanning reinforcement learning, time series modelling, natural language processing and knowledge representation.
Williams W, Prasad N, Mrva D, Ash T, Robinson T, "Scaling Recurrent Neural Network Language Models" IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP 2015)
Abstract: This paper investigates the scaling properties of Recurrent Neural Network Language Models (RNNLMs). We discuss how to train very large RNNs on GPUs and address the questions of how RNNLMs scale with respect to model size, training-set size, computational costs and memory. Our analysis shows that despite being more costly to train, RNNLMs obtain much lower perplexities on standard benchmarks than n-gram models. We train the largest known RNNs and present relative word error rates gains of 18% on an ASR task. We also present the new lowest perplexities on the recently released billion word language modelling benchmark, 1 BLEU point gain on machine translation and a 17% relative hit rate gain in word prediction.
Attached here is my CV.
To get in touch, email me at np6 [at] princeton [dot] edu. You can also find me on: