A technical paper by Veselin Stoyanov and James Mayfield; Tan Xu and Douglas W. Oard; Dawn Lawrie; Tim Oates and Tim Finin. Entity linking refers to the task of assigning mentions in documents to their corresponding knowledge base entities. Entity linking is a central step in knowledge base population. Current entity linking systems do not explicitly model the discourse context in which the communication occurs.
Arjun Kumar and Tim Oates Abstract—Neural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection [1] and time series classification [2]. The internal…
Karan K. Budhraja and Tim Oates Abstract—Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the…
Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi Abstract—As the use of cloud computing continues to rise, controlling cost becomes increasingly important. Yet there is evidence that 30% – 45% of cloud spend is wasted (Weins 2017). Existing tools for cloud provisioning typically rely on highly trained human experts to specify what to monitor, thresholds…
Stephen Sklarew, Tim Oates, PhD Since the dawn of commercial software, product and technology leaders have sought the right balance between research and development and when to launch new products to market. Too much research stymies their ability to get to market fast. Too little research and they often miss the mark; their first mover…
Adrian Rosebrock, Tim Oates, Jesus Caban Abstract—Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring many experimentations and parameter tunings to obtain an adequately performing model. Furthermore training a system in a given domain and then migrating the model to a separate domain will likely yield poor…
Sourav Mukherjee, PhD; Tim Oates, PhD; Ryan Wright The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research.…