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A deep learning approach to lip reading!
This project aims to examine the ideas and methodologies outlined in the paper "Linformer: Self-Attention with Linear Complexity” by Wang et al. (2020)
Learn distribution of feature maps within a geospatial grid, and photos within it:
#movie recommendation #transformer
This model will help indie game developers to generate marketing material that summarizes their game in appealing ways, as well as matching marketing visualizations.
DeepSentiment aims to explore and compare the performance of convolutional and graph-based neural network models for sentiment classification.
The purpose of this project is to apply deep learning techniques to identify bone fractures in X-ray images using ML and DL techniques.
We attempt to essentially beat the market by employing the numerous deep learning concepts we have learned throughout the semester by creating a model that outputs buy and sell signals.
A deep learning model that forecasts the weather
Using Deep Learning, this model will train on a series of credit card transaction data points and be able to classify fraudulent transactions.
Previous work has utilized the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and a multimodal approach to provide diagnostic support. We will be extending this paper to more modalities.
We will use contract details and other financial data, namely the data used in the Black-Scholes Model for options pricing to predict future options prices.
Our project is on patient drug review sentiment analysis, using an extended, novel pre-processing stage and an LSTM classifier as our primary framework.
Revolutionizing Alzheimer’s diagnosis with deep learning, using CNNs and multi-modal data for enhanced accuracy.
Jet tagging identifies particles in collisions. This project uses Transfer Learning to enhance speed and accuracy for CERN's future collider.
GenoNet focuses on cell type annotation on scRNA-seq data. We adjusted the Cellcano using sampling and pseudo-data generation methods.
Finding brain tumors one by one
Upgrade Your Images for Clearer Memories and Rediscover History in HD
This project explores the integration of neural network-based feature extraction with traditional linear regression models to improve prediction accuracy in the social sciences.
We plan to detect seizures and other brain diseases using EEG data with a multimodal deep learning framework. We hypothesize that this will increase both precision and interpretability of the model.
CSCI 1470 Final Project
Detecting Deepfakes with a Fine Tuned VGG-CNN Approach
Object Detection
Developing a model to add creative, contextually relevant, and humorous captions to memes, enhancing engagement and entertainment.
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