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In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence …
Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that …
Deep Gaussian processes (DGP) have appealing Bayesian properties, can handle variable-sized data, and learn deep features. Their …
In partially observable environments, an agent’s policy should often be a function of the history of its interaction with the …
How do we determine whether two or more clothing items are compatible or visually appealing? Part of the answer lies in understanding …
Importance weighted variational inference (Burda et al., 2015) uses multiple i.i.d. samples to have a tighter variational lower bound. …
This paper explores the impact of warnings, audio feedback, and gender on human-robot trust in the context of autonomous driving and …
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and …
Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization …
Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling …