Derivatives and Neural Networks
Project supervisors: Alex Iosevich (UR), Azita Mayeli
(CUNY) and Brian McDonald (UR)
Project description: We are going to study, both
theoretically and empirically, the impact of adding discrete
derivatives of the historical data as regressors in order to
improve the performance of neural network prediction models. In
simple terms, if we give a neural network a sequence of real
numbers and ask it to predict the next few values, how helpful
is it to provide the model with the consecutive difference
(and/or second differences) of the elements of the sequence?
References: https://www.sciencedirect.com/science/article/abs/pii/S0893608005800206
Team: Amy Fang, Josh Iosevich, Anya Myakushina, Svetlana
Pack, Maxwell Sun, and Stephanie Wang
Erdos problems and the Vapnik-Chervonenkis
dimension
Project supervisors: Alex Iosevich (UR), Brian McDonald
(UR) and Emmett Wyman (UR)
Project description: We are going to study the existence
and complexity of finite point configurations in vector spaces
over finite fields using the notion of VC-dimension, and
investigate connections with related notions from learning
theory.
References: arXiv:2203.03046,
arXiv:2108.13231(www.arxiv.org)
Team: James Hanby (RIT), Tran Duy Anh Le
(UR), Maxwell Sun (MIT)
Natural language processing, reinforcement
learning and web scraping
Project supervisor: Alex Iosevich (UR) and Scott Kirila
(Parker Avery)
Project description: We are going to develop a
mechanism to quickly identify which academic department a
given university page belongs to, which news outlet a given
front page story was published in, and similar web scraping
ideas. In the process we are going to develop a productive
interaction between reinforcement learning and support vector
machine methods.
References: https://www.mdpi.com/2078-2489/12/1/38/htm,
https://www.geeksforgeeks.org/top-7-applications-of-natural-language-processing/
Team: Moeed Baradan, Huanyu Chen, Peirong Hao, Yumeng
He, Bowen Jin, Zhizhi Jing, Junfei Liu, Jiayue Meng, Yixu
Qiu, Yukun Yang
Neural networks and sales models with
economic indicators
Project supervisor: Alex Iosevich (UR) and Scott Kirila
(Parker Avery)
Project description: Many sales models starting
returning less than stellar results during the Covid era, in
part because the training data came from before the Covid
period. In this project we are going to take several readily
available data sets containing sales data and try to come up
with the right mix of economic (and other) indicators that
will make predictions as stable as possible across time,
including the Covid period.
References: arXiv:2105.01036
Team: Moeed Baradan, Veronica Chistaya (house price
variant), Ji Fang, Peirong Hao, Bingyi
Liu, Kuixian Wu
Neural networks, approximation and
geometric measure theory
Project supervisors: Alex Iosevich (UR) and Emmett
Wyman (UR)
Project description: Neural networks are "universal
approximators" in that any Lipschitz function can be
approximated by a neural network arbitrarily closely. This is
a fundamental result, but many real-life data sets are not
realistically described by a Lipschitz function because the
Lipschitz condition limits volatility. In this project we are
going to explore the universal approximation in the case when
Lipschitz functions are replaced by more complicated (and
hopefully more realistic) classes of function, such as
function with graphs satisfying a suitable fractal dimension
condition.
References: https://machinelearningmastery.com/neural-networks-are-function-approximators/,
http://neuralnetworksanddeeplearning.com/chap4.html
Team: Amy Fang, Zhizhi Jing, Peter MacNeil, Yixu Qiu,
Rohan Soni, Jake Wellington, Yukun Yang
Optimal location for charging stations for
electric cars
Project supervisors: Alex Iosevich (UR) and Steven
Senger (Missouri State University)
Project description: We are going to build a model to
determine the optimal location for charging stations for
electric cars in Rochester and Ithaca.
References: https://www.sciencedirect.com/science/article/pii/S2352484722001809
Team: Rachel Dennis, Konstantin Dits, Caroline He, Anya
Myakushina
Modeling seizures using machine learning
Project supervisor: Alex Iosevich
Project description: It is widely believed in the
medical community that epileptic seizures do not follow a
particular daily or weekly time patterns. We believe that the
techniques of modern data science have not yet been deployed
in the study of this problem in a systematic way. We are going
to experiment with a variety of techniques, including
reinforcement learning, to look for patterns in the
commercially available data sets.
References: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2739976/,
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5801770/
Team: James Hanby, Marco Minchev, Svetlana Pack
Multi-task learning
Project supervisors: Alex Iosevich
(UR) and Nate Whybra
Project description:
One of the most notable differences between most machine
learning algorithms and humans is that humans have the ability
to do multiple tasks. In order to develop AI that can more
closely mimic human inference and learning capabilities, they
must be able to generalize information from their environment,
and use that information flexibly to perform an arbitrary
number of tasks. We are going to study the idea of building a
large neural network and training it on multiple tasks, then
identify substructures of this large network that achieve the
same efficacy as the large network or neural networks built
for each task individually. We will identify task-model
substructures from the large network using binary masks over
the connections between nodes as well as pruning techniques.
References: -Michael Crawshaw.
“Multi-Task Learning with Deep Neural Networks: A Survey”. In:
(Sept. 2020). url:
http://arxiv.org/abs/2009.09796.
-Shagun Sodhani et al. “Environments and Baseline for
Multitask Reinforcement Learning”. In: (2021). url:
https://ep2021.europython.eu/media/conference/slides/5sUtdJv-multitask-reinforcement-learning-with-python.pdf
-Jonathan Frankle and Michael Carbin. “The Lottery Ticket
Hypothesis: Finding Sparse, Trainable Neural
Networks”. In: (Mar. 2018).
url: http://arxiv.org/abs/1803.03635.
-Hattie Zhou et al. “Deconstructing Lottery Tickets: Zeros,
Signs, and the Supermask”. In: (May 2019). url:
http://arxiv.org/abs/1905.01067.
-Namhoon Lee, Thalaiyasingam Ajanthan, and Philip H. S. Torr.
“SNIP: Single-shot Network Pruning based
on Connection Sensitivity”. In: (Oct. 2018).
url: http://arxiv.org/abs/1810.02340.
-Network Pruning 101. url:
https://towardsdatascience.com/neural-network-pruning-101-af816aaea61
Team: Ryan Hilton, Bowen Jin, Vicky Wang, Kevin Xu,
Zhiyao Xu