A Kernel-Based Calculation of Information on a Metric Space

Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space. This is motivated by the problem in neuroscience of calculating the mutual information between stimuli and spiking responses; the space of these responses is a metric space. It is shown that kernel density estimation on a metric space resembles the k-nearest-neighbor approach. This approach is applied to a toy dataset designed to mimic electrophysiological data

Collaboration and Cognitive Skills in the Workplace: Results from the PIAAC Survey

Using the PIAAC survey, this study examined social characteristics of work and cognitive skills. Results negatively associated collaboration at work and PIAAC scores, contradicting current thinking on workplace interactions

Learning at work in female-dominated and male-dominated industries: A PIAAC study

Learning at work has the potential to be an important contributor to employee performance and professional advancement. Yet, gender inequality is prevalent in many workplaces and may influence the types and quality of learning to which employees are exposed. This study’s purpose was to examine the relationship between female- and male-dominated industries and learning at work as measured by the Program for the International Assessment of Adult Competencies (PIAAC). For those industry sectors determined to be female- or male-dominated, we used a linear regression model to determine whether a relationship exists between gender dominance and learning at work based on the independent variables gender, education level, and race. Results indicate workers in female-dominated industries engage in more learning at work than those in male-dominated industries. We conclude gender-dominance may influence workplace culture and social interactions, thereby affect learning at work

Domain Randomization and Generative Models for Robotic Grasping

Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of domain randomization to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution over possible grasps. This model allows us to sample grasps efficiently at test time (or avoid sampling entirely). We evaluate our model architecture and data generation pipeline in simulation and the real world. We find we can achieve a $>$90% success rate on previously unseen realistic objects at test time in simulation despite having only been trained on random objects. We also demonstrate an 80% success rate on real-world grasp attempts despite having only been trained on random simulated objects.Comment: 8 pages, 11 figures. Submitted to 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018