32 citations · 35 across the 4 of their papers we have counts for
10 papers
VIB is Half Bayes
Alexander A Alemi, Warren R Morningstar, Ben Poole +2
In discriminative settings such as regression and classification there are two random variables at play, the inputs X and the targets Y. Here, we demonstrate that the Variational I…
Cycles in Causal Learning
Katie Everett, Ian Fischer
In the causal learning setting, we wish to learn cause-and-effect relationships between variables such that we can correctly infer the effect of an intervention. While the differen…
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu +4
The Predictive Information is the mutual information between the past and the future, I(X_past; X_future). We hypothesize that capturing the predictive information is useful in RL,…
An Unsupervised Information-Theoretic Perceptual Quality Metric
Sangnie Bhardwaj, Ian Fischer, Johannes Ballé +1
Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to…
Learnability for the Information Bottleneck
Tailin Wu, Ian Fischer, Isaac L. Chuang +1
The Information Bottleneck (IB) method (\cite{tishby2000information}) provides an insightful and principled approach for balancing compression and prediction for representation lea…
Information-Bottleneck Approach to Salient Region Discovery
Andrey Zhmoginov, Ian Fischer, Mark Sandler
We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the ma…