Designing Better Sequence Models with RNNs with Adji Bousso Dieng

Banner Image: Adji Bousso Dieng - Podcast Interview

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About this Episode

In this episode, i'm joined by Adji Bousso Dieng, PhD Student in the Department of Statistics at Columbia University.

In this interview, Adji and I discuss two of her recent papers, the first, an accepted paper from this year's ICML conference titled "Noisin: Unbiased Regularization for Recurrent Neural Networks," which, as the name implies, presents a new way to regularize RNNs using noise injection. The second paper, an ICLR submission from last year titled "TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency," debuts an RNN-based language model designed to capture the global semantic meaning relating words in a document via latent topics. We dive into the details behind both of these papers and I learn a ton along the way.

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