F18-STAT946-Proposal

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).

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Project # 1 Group members:

Zhang, Xinyue

Zhang, Junyi

Chen, Shala

Title: Airbus Ship Detection Challenge

Description: The idea and data for this project is taken from https://www.kaggle.com/c/airbus-ship-detection#description. The goal for this project is to build a model that detects all ships in satellite images and put an aligned bounding box segment around the ships we locate. We are going to extract the segmentation map for the ships first, augment the images and train a simple CNN model to detect them.

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Project # 2 Group members:

Nekoei, Hadi

Afify, Ahmed

Carrillo, Juan

Ganapathi Subramanian, Sriram

Title: Algorithmic Analysis and Improvements in Multi-Agent Reinforcement Learning in Partially Observable Settings

Description: Reinforcement learning (RL) is a branch of Machine Learning in which an agent learns to act optimally in an environment using weak reward signals, which is different from strong labels in supervised learning. Multi-Agent Reinforcement Learning (MARL) is composed of multiple agents that can be competing against each other or cooperating together to achieve a common goal.

Our project aims to investigate the performance of several state of the art Multi-Agent Reinforcement Learning (MARL) algorithms in playing the game of Pommerman. This game will be used as a benchmark during a competition that will be held in NIPS 2018 (https://www.pommerman.com). We plan to participate and compare the performance of our agents against agents created by other researchers. Our project also aims to make algorithmic improvements to the state of the art MARL algorithms and come up with a new algorithm that renders best performance in this partially observable multi-agent setting of Pommerman.

In Pommerman, we have two competing teams, each has two agents who work together to defeat the opponent team. The agents move inside the board leaving bombs that can eliminate other agents when exploding in their horizontal or vertical vicinity. The agents can obtain bonuses such as extra bombs, increased bomb range, or ability to kick installed bombs. Our two agents can choose one of the following actions: stop, move up, move left, move down, move right, or lay a bomb. Each agent will receive an 11x11 grid of integer values representing the board state. Additional information will be provided to the agents such as its own position, positions of his teammate and enemies, available bombs, blast strength, kicking ability, and surrounding walls and bombs.

The algorithms that we are considering are:

- Monte Carlo Tree Search and Reinforcement Learning: Combining MCTS with deep neural networks.

- Multi-Agent Deep Deterministic Policy Gradient (DDPG): A technique developed by OpenAI, based on the Deep Deterministic Policy Gradient technique that outperforms traditional Reinforcement Learning algorithms (DQN/DDPG/TRPO) in several environments.

- Opponent Modelling in Deep Reinforcement Learning: based on DQN to model opponents through a Deep Reinforcement Opponent Network (DRON).

We will use Convolutional Neural Networks for data pre-processing, where we extract features from inputs. We will also be using Feed Forward Deep Networks along with Reinforcement learning frameworks in all the algorithms we implement (Deep Reinforcement learning).

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Project # 3 Group members:

Fisher, Wesley

Pafla, Marvin

Rajendran, Vidyasagar

Title: Deep Reinforcement Learning for Angry Birds

Description: According to Artificial Intelligence (AI) researchers, AI’s performance in the game Angry Birds will exceed human performance in the next 3-4 years [1]. We propose a final project that will hopefully bring us closer to this goal by developing an AI model based on deep reinforcement learning to play the game Angry Birds. While AI has been applied to Angry Birds in the past, there are only a few approaches that utilize deep learning such as in [3]. We plan to implement Yuan et al.’s recommendations by creating an Angry Birds reinforcement learning model with more learning dimensions [3]. To further add novelty to our research, we want to explore the potential of extending our model with evolutionary algorithms [2]. To realize this project, we plan to use an existing implementation of Angry Birds (either https://github.com/estevaofon/angry-birds-python or the one provided for the Angry Birds AI competition which can be found at https://aibirds.org).

References:

[1] Grace, K., Salvatier, J., Dafoe, A., Zhang, B., & Evans, O. (2017). When will AI exceed human performance? Evidence from AI experts. arXiv preprint arXiv:1705.08807.

[2] Risi, S., & Togelius, J. (2017). Neuroevolution in games: State of the art and open challenges. IEEE Transactions on Computational Intelligence and AI in Games, 9(1), 25-41.

[3] Yuan, Y., Chen, Z., Wu, P., & Chang, L. Enhancing Deep Reinforcement Learning Agent for Angry Birds. https://aibirds.org/2017/aibirds_BNU.pdf

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Project # 4 Group members:

Heydari, Nargess

Manuel, Jacob

Ravi, Aravind

Title: Deep Learning for Detection of Steady State Visually Evoked Potentials

Description: Brain Computer Interfaces (BCIs) enable users to control an external device by modulating their neuronal activity. Steady state visual evoked potential (SSVEP) based BCIs are of particular interest due to their high information transfer rate (ITR) and relatively low amount of training required for use. SSVEP responses are elicited when a user focuses on a flickering light source and are observed prominently in the occipitoparietal area of the cortex. These responses manifest as an increase in amplitude of the frequency components of the EEG signal at the stimulus frequency and harmonic frequencies. Therefore, by analyzing the frequency component dominant in the EEG signals recorded from occipitoparietal area, the stimulus with user’s visual engagement can be identified.The goal of this project is to identify and compare deep learning architectures for classifying SSVEP responses to use in BCIs. Different architectures will be compared with state of the art classification methods (e.g. Canonical Correlation Analysis) through a sensitivity analysis of their accuracy across multiple BCI variables (e.g. analysis window size, subject variability, size of training data, etc.). The goal of this comparison is to establish a new system design to support application of Deep Neural Networks in SSVEP-based BCI. The proposed study will be performed on the SSVEP dataset collected by the eBionics Lab at the University of Waterloo

References

N. S. Kwak, K. R. M ̈uller, and S. W. Lee, “A convolutional neural network for steady state visual evoked potential classification under ambulatory environment,” PLoS One, 2017.

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Project # 5 Group members:

Khan, Salman

Naik, Abdul

Koundinya, Shubham

Title: Deep Learning for Image Captioning

Description: Image captioning is the automatic generation of textual descriptions from images. It involves identifying the contents of an image, understanding relationships between what has been detected and generating textual descriptions.

It is a challenging task as it includes both Computer Vision and Natural Language Processing components. Furthermore, an image can be described by multiple text statements. We will explore various state-of-the-art translation models focussing primarily on different ways of describing an image.

References

StyleNet: Generating Attractive Visual Captions with Styles.-https://ieeexplore.ieee.org/document/8099591.

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Project # 6 Group members:

Amirpasha Ghabussi

Kumar, Dhruv

Sahu, Gaurav

Khan, Kashif

Title: Deep learning model for Question Answering & Machine Comprehension

Description: Question answering is a computer science discipline within the fields of information retrieval and natural language processing, which is concerned with building systems that automatically answer questions posed by humans in a natural language.

We will try to improve the accuracy of the models that have shown promising results in most of the highly active datasets such as SQuAD or MS-Marco.

References

[1] Bi-Directional Attention Flow For Machine Comprehension - https://arxiv.org/abs/1611.01603

[2] QANet : Combining Local Convolution With Global Self - Attention For Reading Comprehension - https://arxiv.org/abs/1804.09541

Project # 7 Group members:

Minhas Manpreet Singh

Budnarain Neil

Ameli Soroush

Rezapour Zahra

Title: SELECT VIA PROXY: EFFICIENT DATA SELECTION FOR TRAINING DEEP NETWORKS

Description: We shall be participating in the ICLR Reproducibility Challenge 2019. Abstract: At internet scale, applications collect a tremendous amount of data by logging user events, analyzing text, and collecting images. This data powers a variety of machine learning models for tasks such as image classification, language modeling, content recommendation, and advertising. However, training large models over all available data can be computationally expensive, creating a bottleneck in the development of new machine learning models. In this work, we develop a novel approach to efficiently select a subset of training data to achieve faster training with no loss in model predictive performance. In our approach, we first train a small proxy model quickly, which we then use to estimate the utility of individual training data points, and then select the most informative ones for training the large target model. Extensive experiments show that our approach leads to a 1.6× and 1.8× speed-up on CIFAR10 and SVHN by selecting 60% and 50% subsets of the data, while maintaining the predictive performance of the model trained on the entire dataset. Further, our method is robust to design choices.

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Project # 8 Group members:

Bhatt, Neel

Chen, Henry

Moosa, Johra Muhammad

Title: Fast and Robust Pedestrian Detection: The Successor Of Fused-DNN+Semantic Segmentation

Description: Object Detection in computer vision and image processing deals with identifying semantic objects such as buildings, cars, or humans in digital images and videos. Particularly, pedestrian detection has attracted much research interest in recent years due to its significance in robotics and autonomous driving applications. Consequently, the accuracy of pedestrian detection algorithms has improved significantly, and much of this progress seems to be driven by breakthroughs in Deep Neural Networks (DNNs) and the availability of open source pedestrian datasets. The current state-of-art model being the Fused-DNN+Semantic Segmentation mask, which achieves the lowest log-average miss rate (L-AMR) of 8.2, on the CALTECH pedestrian dataset [1]. While these advancements are impressive, many improvements can be made. For example, existing deep pedestrian detection models tend to rely on hand-crafted features and are generally hard to train. In addition, they seem to perform poorly when image quality is reduced or background interference is high. For this reason, we are proposing to survey deeper into the state-of-art pedestrian detection algorithms and ultimately propose an improved DNN model to address some of the limitations.

Reference:

[1] Du, Xianzhi, et al. "Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection." Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017.

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Project # 9 Group members:

Sigeng Chen

Title: Human Protein Atlas Image Classification

Description: Classify subcellular protein patterns in human cells. It is an active Kaggle Challenge https://www.kaggle.com/c/human-protein-atlas-image-classification

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Project # 10 Group Members: Glen Chalatov, Ronnie Feng, Ki Beom Lee, Patrick Li

Title: Approximation of Lift-and-Project Methods using Large Hidden Layers; A Comparison to Kernel Methods for Manifold Learning

Kernel methods aim to transform features into a higher dimensional space with reasonable computational cost. Using the kernel trick, we can induce nonlinear patterns into our data through linear transformations. We wish to replicate the behaviour of such methods using neural networks. In contrast to autoencoders which perform dimensionality reduction on data, we use the opposite neural network structure to perform dimensionality lift (increase the dimensionality of our data) as follows:

-Feed d-dimensional data into a neural network

-Using hidden layer(s) of dimension p >> d, we attempt to project our data into higher dimensions

-Using a d-dimensional output layer and a loss function that penalizes difference between the output and input, we tune our network and output the p-dimensional hidden layer to arrive at our lifted feature space.

Our project will analyze and contrast the performance of our lifted feature space under a variety of conditions and applications. Our current hypothesis is that these methods will allow for greater flexibility in pattern recognition, but will be more prone to overfitting. If time allows, we will compare our method against traditional lift-and-project ideas from semidefinite optimization.

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Project # 11 Group Members: Zheng Ma, Jiazhen Chen, Ruijie Zhang, Charupriya Sharma

Title: Deep Learning Based Automatic Theorem Prover

Kernel methods aim to transform features into a higher dimensional space with reasonable computational cost. Using the kernel trick, we can induce nonlinear patterns into our data through linear transformations. We wish to replicate the behaviour of such methods using neural networks. In contrast to autoencoders which perform dimensionality reduction on data, we use the opposite neural network structure to perform dimensionality lift (increase the dimensionality of our data) as follows:

-Feed d-dimensional data into a neural network

-Using hidden layer(s) of dimension p >> d, we attempt to project our data into higher dimensions

-Using a d-dimensional output layer and a loss function that penalizes difference between the output and input, we tune our network and output the p-dimensional hidden layer to arrive at our lifted feature space.

Our project will analyze and contrast the performance of our lifted feature space under a variety of conditions and applications. Our current hypothesis is that these methods will allow for greater flexibility in pattern recognition, but will be more prone to overfitting. If time allows, we will compare our method against traditional lift-and-project ideas from semidefinite optimization.