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Designing Better Sequence Models with RNNs with Adji Bousso Dieng - TWiML Talk #160
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Designing Better Sequence Models with RNNs with Adji Bousso Dieng - TWiML Talk #160
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
38 minutes
Released:
Jul 2, 2018
Format:
Podcast episode
Description
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. For complete show notes, visit twimlai.com/talk/160.
Released:
Jul 2, 2018
Format:
Podcast episode
Titles in the series (100)
This Week in Machine Learning & AI - 6/17/16: Apple's New ML APIs, IBM Brings Deep Learning Thunder: This Week in Machine Learning & AI brings you the… by The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)