By Alicia Wang, Arjun Khare, Arsh Zahed, James Jiao, Olivia Jain, Nareg Megan and Reina Wang
The goal of this project was to be able to build a network that would be able to answer a question about a given image.
This task can be broken down into three parts: image embeddings, word embeddings, and recurrent attention.
The first model we tried is relatively simple. We use the K-Nearest Neighbors algorithm to find the most suitable answer to a given image, question pair in the following process. Using the question embeddings we find the K-nearest questions to the test question…
Current weather predictions in the industry involve measurements of data such as humidity, temperature, wind speed, etc. that are inputted into physics equations to be computed for predictions. While a reliable method, it is an extremely computational process that can take days to computer, which also makes the predictions independent of more recent weather data and suffer from a natural range of predictions.
The goal of this project is to test current ML research in video frame prediction on weather prediction. Particularly, we will be using consecutive radar images that are fed into a model to extract weather patterns…
The standard set of game-level basketball statistics include but are not limited to points, rebounds, blocks, etc. Though these statistics relay valuable information about a team’s performance across a game, often more advanced statistics are computed such as a team’s offensive efficiency which is defined to be the total points scored normalized by the total number of possessions. These statistics were pulled using an NBA API that spans games from the late ’70s to the current season.
While non-linear models often tend to perform well for regression tasks, linear models offer more interpretability of features, which is a great option…
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