F21-STAT 940-Proposal

From statwiki
Revision as of 13:46, 9 October 2020 by Mhwu (talk | contribs)
Jump to: navigation, search

Use this format (Don’t remove Project 0)

Project # 0 Group members:

Last name, First name

Last name, First name

Last name, First name

Last name, First name

Title: Making a String Telephone

Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).



Project # 1 Group members:

McWhannel, Pierre

Yan, Nicole

Hussein Salamah, Ahmed

Title: placeholder

Description: placeholder


Project # 2 Group members:

Singh, Gursimran

Sharma, Govind

Chanana, Abhinav

Title: Quick Text Description using Headline Generation and Text To Image Conversion

Description: An automatic tool to generate short description based on long textual data is a useful mechanism to share quick information. Most of the current approaches involve summarizing the text using varied deep learning approaches from Transformers to different RNNs. For this project, instead of building a standard text summarizer, we aim to provide two separate utilities for generating a quick description of the text. First, we plan to develop a model that produces a headline for the long textual data, and second, we are intending to generate an image describing the text.

Headline Generation - Headline generation is a specific case of text summarization where the output is generally a combination of few words that gives an overall outcome from the text. In most cases, text summarization is an unsupervised learning problem. But, for the headline generation, we have the original headlines available in our training dataset that makes it a supervised learning task. We plan to experiment with different Recurrent Neural Networks like LSTMs and GRUs with varied architectures. For model evaluation, we are considering BERTScore using which we can compare the reference headline with the automatically generated headline from the model. We also aim to explore attention models for the text (headline) generation. We will make use of the currently available techniques mentioned in the various research papers but also try to develop our own architecture if the previous methods don't reveal reliable results on our dataset. Therefore, this task would primarily fit under the category of application of deep learning to a particular domain, but could also include some components of new algorithm design.

Text to Image Conversion - Generation or synthesis of images from a short text description is another very interesting application domain in deep learning. One approach for image generation is based on mapping image pixels to specific features as described by the discriminative feature representation of the text. Recurrent Neural Networks have been successfully used in learning such feature representations of text. This approach is difficult to generalize because the recognition of discriminative features for texts in different domains is not an easy task and it requires domain expertise. Different generative methods have been used including Variational Recurrent Auto-Encoders and its extension in Deep Recurrent Attention Writer (DRAW). We plan to experiment with Generative Adversarial Networks (GAN). Application of GANs on domain-specific datasets has been done but we aim to apply different variants of GANs on the Microsoft COCO dataset which has been used in other architectures. The analysis will be focusing on how well GANs are able to generalize when compared to other alternatives on the given dataset.

Scope - The above models will be trained independently on different datasets. Therefore, for a particular text, only one of the two functionalities will be available.


Project # 3 Group members:

Sikri, Gaurav

Bhatia, Jaskirat

Title: Not decided yet (Placeholder)

Description: Not decided yet :)


Project # 4 Group members:

Maleki, Danial

Rasoolijaberi, Maral

Title: Binary Deep Neural Network for the domain of Pathology

Description: The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. We want to investigate the possibility of using these types of networks in the domain of histopathology as it has gigapixels images which make the use of them very useful.


Project # 5 Group members:

Jain, Abhinav

Bathla, Gautam

Title: lyft-motion-prediction-autonomous-vehicles(Kaggle)(Tentative)

Description: Autonomous vehicles (AVs) are expected to dramatically redefine the future of transportation. However, there are still significant engineering challenges to be solved before one can fully realize the benefits of self-driving cars. One such challenge is building models that reliably predict the movement of traffic agents around the AV, such as cars, cyclists, and pedestrians.

Comments: We are more inclined towards a 3-D object detection project. We are in the process of finding the right problem statement for it and if we are not successful, we will continue with the above Kaggle competition.


Project # 6 Group members:

You, Bowen

Avilez, Jose

Mahmoud, Mohammad

Wu, Mohan

Title: Deep Learning Models in Volatility Forecasting

Description: Price forecasting has become a very hot topic in the financial industry in recent years. We are however very interested in the volatility of such financial instruments. We propose a new deep learning architecture or model to predict volatility and apply our model to real life datasets of various financial products. We will analyze our results and compare them to more traditional methods.