Deep networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. While these models enable superior performance, their increasing complexity and lack of decomposability into individually intuitive ...

The widespread use of internet-connected mobile devices, internet of things(IoT) and cloud computing has enabled a large scale collection of personal data, including user profiles, daily activities, locations, photos and ...

In this dissertation, I propose and study a multi-modal Artificial Intelligence (AI) task called Visual Question Answering (VQA) -- given an image and a natural language question about the image (e.g., "What kind of store ...

In this work we propose a new approach to the object detection problem using Deep Neural Network, in the context of traffic sign detection. Our approach simplifies the detection head complexity by making the requirement ...

Deep learning (DL) models require large labeled datasets for training. Practitioners often need to adapt an existing DL model to a different domain. For instance, a practitioner in a company developing autonomous vehicles ...

Artificial Intelligence research has progressed tremendously in the last few years. There has been the introduction of several new multi-modal datasets and tasks due to which it is becoming much harder to compare new ...

Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own or in combination with reinforcement learning, it enables agents ...

Graph structures, like syntax trees, social networks, and programs, are ubiquitous in many real world applications including knowledge graph inference, chemistry and social network analysis. Over the past several decades, ...

The world around us involves multiple modalities -- we see objects, feel texture, hear sounds, smell odors and so on. In order for Artificial Intelligence (AI) to make progress in understanding the world around us, it needs ...

Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to data center resource management and matching in ride-sharing services, decisions are often ...