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AI based approach to Trailer Generation for Online Educational Courses
It also helps to generate curiosity and interest among the learners and encourages them to pursue a course.
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships
However, most of the existing KG embeddings only consider the network structure of the graph and ignore the semantics and the characteristics of the underlying ontology that provides crucial information about relationships between entities in the KG.
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning
Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined.
Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation
In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds.
Learning agents with prioritization and parameter noise in continuous state and action space
Deep Q-learning networks (DQN) and Deep Deterministic Policy Gradient (DDPG) are two such methods that have shown state-of-the-art results in recent times.
Human Trajectory Prediction using Spatially aware Deep Attention Models
All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects.
Phoenix: A Self-Optimizing Chess Engine
With the advent of deep learning, chess playing agents can surpass human ability with relative ease.
