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Contents
About Deepmath 3
Important Information 4
Timetable 5Thursday, Nov 5 . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 5Friday, Nov 6 . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5List of Posters 6List of Poster Presentations . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . 6Sponsors 10
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About Deepmath
Recent advances in deep neural networks (DNNs), combinedwith
open, easily-accessible implemen-tations, have made DNNs a
powerful, versatile method used widely in both machine learning
andneuroscience. These advances in practical results, however, have
far outpaced a formal understand-ing of these networks and their
training. The dearth of rigorous analysis for these techniques
limitstheir usefulness in addressing scientific questions and, more
broadly, hinders systematic design ofthe next generation of
networks. Recently, long-past-due theoretical results have begun to
emergefrom researchers in a number of fields. The purpose of this
conference is to give visibility to theseresults, and those that
will follow in their wake, to shed light on the properties of
large, adaptive,distributed learning architectures, and to
revolutionize our understanding of these systems.
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Important Information
• The conference venue The focal point of the event is our
virtual conference hall, whichyou can access at
https://gather.town/app/AceGf3n47aKkqZ1P/DeepMath2020 (login with
the same email you used to register for the conference).• Posters.
The poster sessions are at 2pm-4pm (14:00-16:00) EST on Thursday
and 11:45am-1:45pm (11:45-13:45) EST on Friday. Poster presenters
are asked to be at their posters foreven-numbered posters on
Thursday and odd-numbered ones on Friday.• Best poster voting: We
are crowd-sourcing votes for the DeepMath 2020 best poster andbest
student poster (with a secret prize to be announced after the
conference). Please votefor your favorite poster at
https://forms.gle/tZwFjSNpB7LqQtVL6.
Questions / Comments / Concerns? Contact one of the organizers
in Gather, or email [email protected].
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https://gather.town/app/AceGf3n47aKkqZ1P/DeepMath2020https://forms.gle/tZwFjSNpB7LqQtVL6mailto:[email protected]:[email protected]
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Timetable
Thursday, Nov 5
Friday, Nov 6
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List of Posters
List of Poster Presentations
ID 1: The Recurrent Neural Tangent KernelSina Alemohammad*;
Randall Balestriero; Zichao Wang; Richard Baraniuk , Rice
UniversityID 2: Provable regression with nets of finite sizeAnirbit
Mukherjee*; Ramchandran Muthukumar, Johns Hopkins UniversityID 3: A
Game Theoretic Analysis of Additive Adversarial Attacks and
DefensesAmbar Pal*; Rene Vidal, Johns Hopkins UniversityID 4:
Minimal Adversarial Perturbations via Eikonal EquationsNurislam
Tursynbek*; Ivan Oseledets, Skolkovo Institute of Science and
TechnologyID 5: Learning Interpretable Representations using
Operators Distributed acrossthe Latent SpaceStephane Deny*; Diane
Bouchacourt; Mark Ibrahim, Stanford and Facebook AI ResearchID 6: A
nonparametric model of independence for cognitive feature
representa-tionsGregory Henselman-Petrusek*; Tyler Giallanza; Simon
Segert; Sebastian Musslick; JonathanCohen , Princeton UniversityID
7: Random feature networks with neuronal tuningBiraj Pandey*;
Kameron Decker Harris; Bingni Brunton, University of WashingtonID
8: Effective Theory of Multilayer PerceptronsBoris Hanin; Daniel A
Roberts*; Sho Yaida, Texas A&M, MIT, and Facebook ResearchID 9:
The Gaussian Equivalence of generativemodels for learningwith
two-layerneural networksSebastian Goldt*; Galen Reeves; MarcMézard;
Florent Krzakala; Lenka Zdeborova, Laboratoirede physique
théorique, Ecole normale supérieure, Paris
ID 10: Error Estimates of Several Physics-informed Neural
NetworksZhongqiang Zhang*; Yeonjong Shin); George
Karniadakis,Worcester Polytechnic Institute andBrown University
ID 11: Theoretical Analysis of the Representational Power of
GANs and FlowModelsFrederic Koehler; Viraj Mehta; Andrej
Risteski*,MIT and CMU
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ID 12: To interact or not? The convergence properties of
interacting stochasticmirror descentAnastasia Borovykh*; Nikolas
Kantas; Panos Parpas; Grigorios Pavliotis , Imperial
CollegeLondon
ID 13: Analytic Characterization of the Hessian Spectral Density
in Shallow ReLUModels: A Tale of SymmetryYossi Arjevani*; Michael
Field, NYU and UCSBID 14: Interpreting Deep Learning Models: Flip
Points and Homotopy MethodsRoozbeh Yousefzadeh*; Dianne O’Leary;
Furong Huang, Yale UniversityID 15: Activation function dependence
of the storage capacity of treelike neuralnetworksJacob A
Zavatone-Veth*; Cengiz Pehlevan, Harvard UniverityID 16: Guarantees
for stable signal and gradient propagation in
self-attentiverecurrent networksGiancarlo B Kerg*; Bhargav
Kanuparthi; AnirudhGoyal; Kyle Goyette; Yoshua Bengio;
GuillaumeLajoie,MILAID 17: Consistency of a Recurrent Language
Model With Respect to IncompleteDecodingSean Welleck*; Ilia
Kulikov; Jaedeok Kim ; Richard Yuanzhe Pang; Kyunghyun Cho , New
YorkUniversity
ID 18: Learning a Lie Algebra from Unlabeled Data
PairsChristopher A Ick*; Vincent Lostanlen, New York UniversityID
19: MomentumRNN: IntegratingMomentum into Recurrent Neural
NetworksTan Minh Nguyen*; Richard Baraniuk; Andrea L. Bertozzi;
Stanley Osher ; Bao Wang, RiceUniversity
ID 20: Complexity Bounds forNeuralNetworkswith EncodableWeights
in Smooth-ness SpacesIngo Gühring*; Mones Raslan, Technische
Universität BerlinID 21: On 1/n neural representation and
robustnessJosue Nassar*; Piotr Sokol; Sueyeon Chung; Kenneth
Harris; Memming Park, Stony BrookUniversity
ID 22: A theory of robust and flexible sequencing through
multi–dimensionaltransients controlled by low-rank connectivity
perturbations in recurrent net-worksLaureline Logiaco*; Larry
Abbott; G Sean Escola, Columbia UniversityID 23: Identifying
stochastic oracles for fast convergence of RMSPropJiayao Zhang*;
Anirbit Mukherjee, University of Pennsylvania
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ID 24: LOCA: LOcal Conformal AutoencoderOfir Lindenbaum*; Erez
Peterfreund; Tom Bertalan; Felix Dietrich; Matan Gavish;
IoannisKevrekidis; Ronald Coifman, Yale UniversityID 25: Analytical
aspects of non-differentiable neural networksMatteo Spallanzani*;
Gian Paolo Leonardi, ETH ZurichID 26: Extendable and invertible
manifold learning with geometry regularizedautoencodersAndres F
Duque*; Sacha Morin; Guy Wolf; Kevin Moon, Utah State UniversityID
27: Generalization of Kernel Regression for Realistic DatasetsBlake
A Bordelon*; Abdulkadir Canatar; Cengiz Pehlevan, Harvard
UniversityID 28: The interplay between randomness and structure
during learning in RNNsFriedrich Schuessler*; Francesca
Mastrogiuseppe; Alexis Dubreuil; Srdjan Ostojic); Omri
Barak,Technion
ID 29: Scheduled Restart Momentum for Accelerated Stochastic
Gradient De-scentBao Wang; Tan Minh Nguyen*; Tao Sun; Andrea L.
Bertozzi; Richard Baraniuk; Stanley Osher,University of Utah and
Rice University
ID 30: Neural Anisotropy DirectionsGuillermoOrtiz-Jimenez*;
ApostolosModas; Seyed-MohsenMoosavi-Dezfooli; Pascal
Frossard,EPFL
ID 31: Quantifying dynamic stability and signal propagation:
Lyapunov spectraof chaotic recurrent neural networksRainer
Engelken*; Fred Wolf; Larry Abbott, Columbia UniversityID 32:
Multiple Timescales of Activity in Recurrent Neural Networks with
Clus-tered ConnectivityMerav Stern*; Nicolae Istrate; Luca
Mazzucato, University of OregonID 33: Gradient Starvation: A Theory
of Learning in Neural NetworksMohammad Pezeshki*; Guillaume Lajoie;
Yoshua Bengio ; Aaron Courville; Doina Precup,Mila
ID 34: Exact Polynomial-time Convex Optimization Formulations
for ReLU Net-worksTolga Ergen*; Mert Pilanci, Stanford UniversityID
35: Scattering Priors for Graph Neural NetworksAlexander Y Tong*;
Frederik Wenkel; Kincaid Macdonald; Guy Wolf; Smita Krishnaswamy,
YaleUniversity
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ID 36: When and How Can Deep Generative Models be Inverted?Aviad
Aberdam*; Dror Simon; Michael Elad, TechnionID 37: Pruning neural
networkswithout any data by iteratively conserving synap-tic
flowHidenori Tanaka; Daniel Kunin*; Daniel Yamins; Surya Ganguli,
NTT Reserach, Inc./StanfordUniversity
ID 38: Adaptivity of ReLU nets beyond low-dimensional
domainsTimo Klock*; Alexander Cloninger, Simula Research Lab/UCSDID
39:
UnderstandingRecurrentNeuralNetworksUsingNonequilibriumResponseTheorySoon
Hoe Lim*, NorditaID 40: Geometry and Optimization of Shallow
Polynomial NetworksMatthew Trager*; Joe Kileel; Yossi Arjevani;
Joan Bruna, NYUID 41: RNNs can generate bounded hierarchical
languages with optimal memoryJohn Hewitt*; Michael Hahn ; Surya
Ganguli; Percy Liang; Christopher D. Manning,
StanfordUniversity
ID 42: Learning high-dimensional Hilbert-valued functions with
deep neural net-works from limited data.Sebastian A Moraga
Scheuermann*; Nick Dexter; Simone Brugiapaglia; Ben Adcock ,
SimonFraser University
ID 43: Traversing the Optimization Landscape of Deep Neural
Networks: HowGood is Gradient Descent?Waheed Bajwa*; Rishabh Dixit,
Rutgers UniversityID 44: Generalisation error in learning with
random features and the hiddenmanifold modelFederica Gerace*; Bruno
Loureiro; Florent Krzakala; Marc Mézard; Lenka Zdeborova,
Politec-nico di Torino
ID 45: Stability-performance barriers and practical function
approximation withdeep neural networksNick Dexter*; Simone
Brugiapaglia; Ben Adcock, Simon Fraser University
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Sponsors
The Deepmath conference would like to thank the Simons
Foundation and the Princeton Neuro-science Institute for
sponsorship for this year’s virtual conference.
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About DeepmathImportant InformationTimetableThursday, Nov
5Friday, Nov 6
List of PostersList of Poster Presentations
Sponsors
