AI School is a mobile app designed to teach the basics of artificial intelligence, machine learning, and deep learning. Includes a comprehensive lesson plan for learning fundamental principles.
We demonstrate the efficacy of a new neural dialogue agent that is able to effectively sustain grounded, multi-domain discourse through a novel key-value retrieval mechanism.
We show that models with domain-specific grounding can effectively realize the pragmatic reasoning that is necessary for more robust natural language interaction.
To model both structured knowledge and unstructured language in a novel dialogue setting, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses.
We show the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism, outperforming more sophisticated models on a standard task-oriented dialogue dataset.
We present SceneSeer: an interactive text to 3D scene generation system with a learned spatial knowledge base that allows a user to design 3D scenes using natural language.
We investigate neural memory network architectures for the task of natural language inference and propose models for using attention across relevant semantic phrases to inform common sense reasoning.
Hitting Depth: Investigating Robustness to Adversarial Examples in Deep Convolutional Neural Networks
We show a process for visualizing and identifying changes in activations between adversarial images and their regular counterparts and propose a Bayesian framework for improving prediction accuracy on adversarial examples.
We implement a random forest classifier with a carefully engineered and selected collection of linguistic and semantic features for the task of natural language inference, achieving an F1 of 80.9% on the SemEval-2014 Dataset.