I will examine how procedures for optimally searching through "multiplex" networks (networks made of multiple simple graphs) capture human learning and search patterns. Prior work on semantic memory (people's memory for facts and concepts) has primarily focused on modeling similarity judgments of pairs of words as distances between points in a high-dimensional space (e.g., LSA by Landauer et al, 1998; Word2Vec by Mikolov et al. 2013).
MadHacks Mini is a 12-hour hackathon filled with food, fun, and code. All projects are welcome! We’ve enlisted mentors to help with your projects, scheduled tech-talks to get the ideas flowing, and arranged for some awesome prizes!
Web applications are integral to today’s society, hosting a variety of services ranging from banking and e-commerce to mapping and social media. To support these rich services, web applications have evolved into complex distributed systems, making critical tasks such as performance optimization and debugging difficult.
Russian Flagship Program | University of Wisconsin-Madison
1322 Van Hise Hall, 1220 Linden Drive | Madison, WI 53706 email@example.com | www.russianflagship.wisc.edu
Website/Interactive Media Student Assistant
POSITION DESCRIPTION AND RESPONSIBILITIES
Assist with designing and developing a revised website for the UW-Madison Russian Flagship Program by:
As biobanks continue to grow and more and more human genomes are sequenced, our ability to detect relationships between genetic variants and diseases is at an unprecedented level. The exponential growth of biological data, including both genetic and health record data, has led to the development of association-based studies (GWAS and PheWAS) that paved the way for identifying links between genetic variations and the development of diseases.
Seeing the Unseen: Data-Driven 3D Scene Understanding
Abstract: Intelligent robots require advanced vision capabilities to perceive and interact with the real physical world. While computer vision has made great strides in recent years, its predominant paradigm still focuses on analyzing image pixels to infer 2D output representations (bounding boxes, segmentations, etc.), which remain far from sufficient for real-world robotics applications.