4 code implementations • ICML 2020 • Laura Rieger, Chandan Singh, W. James Murdoch, Bin Yu
For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective.
4 code implementations • 18 May 2019 • Summer Devlin, Chandan Singh, W. James Murdoch, Bin Yu
Tree ensembles, such as random forests and AdaBoost, are ubiquitous machine learning models known for achieving strong predictive performance across a wide variety of domains.
6 code implementations • 14 Jan 2019 • W. James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, Bin Yu
Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv. org/abs/1806. 05337
1 code implementation • ICLR 2019 • Chandan Singh, W. James Murdoch, Bin Yu
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables.
4 code implementations • ICLR 2018 • W. James Murdoch, Peter J. Liu, Bin Yu
On the task of sentiment analysis with the Yelp and SST data sets, we show that CD is able to reliably identify words and phrases of contrasting sentiment, and how they are combined to yield the LSTM's final prediction.
no code implementations • 8 Feb 2017 • W. James Murdoch, Arthur Szlam
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear.
no code implementations • 12 Dec 2014 • W. James Murdoch, Mu Zhu
We propose a general technique for improving alternating optimization (AO) of nonconvex functions.