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We look to implement real-value non-volume preserving transformations (https://arxiv.org/pdf/1605.08803.pdf) to learn distribution parameters through maximum likelihood.
We are detecting suicidal intent in tweets using a time-aware transformer based model. This incorporates both the tweet itself and past history of a user's tweets.
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A vector-based generation model that predicts the next possible keyframes for animation given an SVG input in real-time.
We will combine our knowledge of deep learning from this course and our background in reinforcement learning to create deep reinforcement learning models to solve the tasks in SC2 video game.
Keep walking...
We analyze stonk price patterns in order to predict future price gyrations in a volatile and unpredictable global market (2021-?)
Deep Learning for Weather Related PDEs
In this work, we compare the use of discrete filtered CNNS versus continuous filters, particularly for quantum chemistry problems.
We use autoencoders to encode and a neural network to classify genres of music, and then borrow ideas from Deep Dream to run the network backwards and alter the encodings to fit a specific genre.
This project investigates the deformation of 3D shapes by using a point cloud interpolation sequence, which is generated via a point cloud GAN.
Finding an optimization strategy to play the game called Jstris, a game is an adaptation of tetris with some additional rules such as an ability to attack other players or techniques such as T-spin.