Hypothesis testing of random networks is an emerging area of modern research, especially in the high-dimensional regime, where the number of samples is smaller or comparable to the size of the graph.
To reduce the cost of training such large models, prior work has developed smaller, more compact models which achieves a significant speedup in training time while maintaining competitive accuracy to the original model on downstream tasks.
Executing natural language instructions in a physically grounded domain requires a model that understands both spatial concepts such as “left of” and “above”, and the compositional language used to identify landmarks and articulate instructions relative to them.
This way the concept learning problem is naturally a program synthesis problem and our algorithm learns from a few examples to synthesize a program representing the novel concept.
We consider the problem of Vision-and-Language Navigation (VLN).
In the recent past a certain property of neural training trajectories in weight-space had been isolated, that of "local elasticity" ($\srel$) - which attempts to quantify the propagation of influence of a sampled data point on the prediction at another data point.
We propose a natural extension of the PA algorithm that uses multiple orthogonal translation matrices to model the mapping and derive an algorithm to learn these multiple matrices.
Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general.
To exhibit the applicability of our representation scheme, we annotate text taken from diverse datasets and show how we extend the capabilities of existing spatial representation languages with the fine-grained decomposition of semantics and blend it seamlessly with AMRs of sentences and discourse representations as a whole.
We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article.
Agents that communicate back and forth with humans to help them execute non-linguistic tasks are a long sought goal of AI.
Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas.