GGHM is a leading global casino gaming industry advisory, analytics and investment firm dedicated to providing its clients with trusted, highly-focused and comprehensive services that help them achieve their strategic objectives.

GGHM is a leading global gaming industry advisory, analytics and investment firm dedicated to providing its clients with trusted, highly-focused and comprehensive services that help them achieve their strategic objectives.

GGHM is a leading global gaming industry advisory, analytics and investment firm dedicated to providing its clients with trusted, highly-focused and comprehensive services that help them achieve...

2023年8月18日 · In this paper, we propose GgHM, a new framework with Graph-guided Hybrid Matching. Concretely, we learn task-oriented features by the guidance of a graph neural network during class prototype construction, optimizing the intra- …

GGH Morowitz applies a risk oriented and best practices approach to the analytics and industry knowledge process that includes a macro and micro approach to data gathering, the development of benchmark comparisons, applying inferential and trend statistical analyses as well as industry standard analyses such as gravity models and other market as...

Official repository for our paper, "Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching."Code coming soon!

GGHM is a leading global gaming industry advisory, analytics and investment firm dedicated to providing its clients with trusted, highly-focused and transparent services that help them achieve their strategic objectives.

GGHM Partner Cory Morowitz recently joined Roger Gros to discuss current trends in the gaming industry. To listen to the podcast, click on the link…

The GGHM is an advanced multi-modal travel demand and forecasting model to support analysis of long-term travel choices across the transportation system in the Greater Golden Horseshoe Region (GGH). The long-term multi-modal travel demand model integrates auto passenger, transit, active transportation and goods movement under a single framework.

method for few-shot action recognition, dubbed GgHM, a short for Graph-guided Hybrid Matching. Specifically, we apply a graph neural network (GNN) for constructing task-oriented features, as shown in Fig.1(b). It could interac-tively transfer information between video features in a task to enhance the prior knowledge of the unknown video. We