328 citations · 1.6k across the 61 of their papers we have counts for
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Inductive Biases for Deep Learning of Higher-Level Cognition
Anirudh Goyal, Yoshua Bengio
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was corre…
On Disentangled Representations Learned From Correlated Data
Frederik Träuble, Elliot Creager, Niki Kilbertus +5
The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often…
The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
Anirudh Goyal, Yoshua Bengio, Matthew Botvinick +1
In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. Th…
Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal +3
Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive…
Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
Samarth Sinha, Zhengli Zhao, Anirudh Goyal +2
We introduce a simple (one line of code) modification to the Generative Adversarial Network (GAN) training algorithm that materially improves results with no increase in computatio…