TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Conditional probabilities are a core concept in machine learning. For ex-<lb>ample, optimal prediction of a label Y given an inputX corresponds to maximizing<lb>the conditional probability of Y given X . A common approach to inference tasks<lb>is learning a model of conditional probabilities. However, these models are often<lb>based on strong assumptions (e.g., log-linear models), and hence their estimate of<lb>conditional probabilities is not robust and is highly dependent on the validity of<lb>their assumptions.<lb>Here we propose a framework for reasoning about conditional probabilities without<lb>assuming anything about the underlying distributions, except knowledge of their<lb>second order marginals, which can be estimated from data. We show how this<lb>setting leads to guaranteed bounds on conditional probabilities, which can be calcu-<lb>lated efficiently in a variety of settings, including structured-prediction. Finally, we<lb>apply them to semi-supervised deep learning, obtaining results competitive with<lb>variational autoencoders.

SOLUTION: Robust Conditional Probabilities

PROBLEM: We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multiagent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.

SOLUTION: Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

PROBLEM: The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDARvideo inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.

SOLUTION:
Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks