Day 15: Continuing sentiment analysis

Today I continued to look into how a sentiment analyst algorithm works. I found that the algorithm utilized a Gated Recurrent Unit (GRU) in order to classify the data it was given. A GRU has an update gate and a reset gate. The reset gate chooses how much of the old data to forget and an update gate decides how to add in the new data. This is important in sentiment analysis because a sentence must be analyzed as a whole not piece by piece. It needs to be analyzed as a whole because it is hard to tell if a sentence is positive or negative if you forget the previous word you fed in. By the end of the day I had gained a decent understanding of  the VQA basics.

Comments

Post a Comment

Popular posts from this blog

Day 11: Network success and an interesting lecture

Image
I started today debugging my code for my neural network. After a healthy amount of googling and some perseverance I was able to roughly train and evaluate my model. The performance was subpar but hopefully I will be able to improve that next week. When that was over I went to the free pizza lunch provided for all of the REU summer research students and I then attended a lecture on visual perception. Although this was not very related to my project I still found it to be a valuable use of my time. It was extremely mind-blowing to realize that our eyes can only see details in a very small field of vision. Our peripheral vision is a lot worse than I previously thought. One of the coolest visual demos was these two tables shown below. Believe it or not the tabletops are exactly the same size and shape. Human visual perception is fascinating and I plan to continue attending the lectures throughout the summer. Source:  http://www.optical-illusionist.com/illusions/table-size-illusio
Read more

Day 26: Polishing my presentation

While I was waiting for my experiments to run on the server I spent the day presenting in front of various members of kLab. I got some very good advice from Dr. Kanan in the morning which I used to update my presentation. Later in the day with the help of  Tyler, Dr. Kemker and Kushal I was able to get a presentation that I was happy with. I plan to update the presentation with the results from the experiments I ran.
Read more

Day 22: Implementing new lifelong learning models

Today I implemented a reservoir sampling algorithm as well as a queue nearest neighbor program. The reservoir algorithm functions similarly to the incremental nearest neighbor program i implemented on Friday. The only difference is that it only holds on to a certain amount of data points per class and when new data points are added it removes an existing point at random. This is an effective way to update your prediction function and it uses less memory than the standard nearest neighbor function. The other program I implemented was the queue nearest neighbor program. This is similar to the reservoir program. The thing queue does differently is that it removes the oldest data point for a specific class when new data points are added. This is also an effective way to reduce memory usage. Tomorrow I plan to combine the VQA data set and these classifiers in order to test the effectiveness of lifelong learning on VQA.
Read more