We challenge the upper bound of human-mobility predictability that is widely used to
corroborate the accuracy of mobility prediction models. We observe that extensions of …

Mobility datasets are fundamental for evaluating algorithms pertaining to geographic
information systems and facilitating experimental reproducibility. But privacy implications …

Training deep neural networks on well-understood dependencies in speech data can
provide new insights into how they learn internal representations. This paper argues that …

Interpretability methods for neural networks are difficult to evaluate because we do not
understand the black-box models typically used to test them. This paper proposes a …

This paper models phonetic and phonological learning as a dependency between random
space and generated speech data in the Generative Adversarial Neural network architecture …

In order to successfully model Long Distance Dependencies (LDDs) it is necessary to
understand the full-range of the characteristics of the LDDs exhibited in a target dataset. In …

From theoretical linguistic and cognitive perspectives, situated dialog systems are
interesting as they provide ideal test-beds for investigating the interaction between language …

In order to build efficient deep recurrent neural architectures, it is essential to analyze the
complexityof long distance dependencies (LDDs) of the dataset being modeled. In this …

This paper proposes a grammatical inference algorithm to learn input-sensitive tier-based
strictly local languages across multiple tiers from positive data only, when the locality of the …

Highly-parameterized deep neural networks are known to have strong data-memorization
capability, but does this ability to memorize random data also extend to simple standard …