
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?
Learning to plan for long horizons is a central challenge in episodic re...
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Towards Understanding the Role of OverParametrization in Generalization of Neural Networks
Despite existing work on ensuring generalization of neural networks in t...
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Provably Efficient Exploration for RL with Unsupervised Learning
We study how to use unsupervised learning for efficient exploration in r...
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When is Particle Filtering Efficient for POMDP Sequential Planning?
Particle filtering is a popular method for inferring latent states in st...
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Dense Associative Memory for Pattern Recognition
A model of associative memory is studied, which stores and reliably retr...
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Stabilizing GAN Training with Multiple Random Projections
Training generative adversarial networks is unstable in highdimensions ...
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Dense Associative Memory is Robust to Adversarial Inputs
Deep neural networks (DNN) trained in a supervised way suffer from two k...
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The importance of quantum decoherence in brain processes
Based on a calculation of neural decoherence rates, we argue that that t...
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Toward a unified theory of sparse dimensionality reduction in Euclidean space
Let Φ∈R^m× n be a sparse JohnsonLindenstrauss transform [KN14] with s n...
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A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and ex...
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General Theory of Image Normalization
We give a systematic, abstract formulation of the image normalization me...
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Barriers for Rank Methods in Arithmetic Complexity
Arithmetic complexity is considered simpler to understand than Boolean c...
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Bridging ManyBody Quantum Physics and Deep Learning via Tensor Networks
The harnessing of modern computational abilities for manybody wavefunc...
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Efficient algorithms for tensor scaling, quantum marginals and moment polytopes
We present a polynomial time algorithm to approximately scale tensors of...
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Generalized comparison trees for pointlocation problems
Let H be an arbitrary family of hyperplanes in ddimensions. We show th...
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Spanoids  an abstraction of spanning structures, and a barrier for LCCs
We introduce a simple logical inference structure we call a spanoid (gen...
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Quantum asymptotic spectra of graphs and noncommutative graphs, and quantum Shannon capacities
We study several quantum versions of the Shannon capacity of graphs and ...
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Gradient Descent Happens in a Tiny Subspace
We show that in a variety of largescale deep learning scenarios the gra...
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Barriers for fast matrix multiplication from irreversibility
The determination of the asymptotic algebraic complexity of matrix multi...
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Subspace arrangements, graph rigidity and derandomization through submodular optimization
This paper presents a deterministic, strongly polynomial time algorithm ...
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An exponential lower bound for the degrees of invariants of cubic forms and tensor actions
Using the Grosshans Principle, we develop a method for proving lower bou...
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More barriers for rank methods, via a "numeric to symbolic" transfer
We prove new barrier results in arithmetic complexity theory, showing se...
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The asymptotic induced matching number of hypergraphs: balanced binary strings
We compute the asymptotic induced matching number of the kpartite kuni...
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Search problems in algebraic complexity, GCT, and hardness of generator for invariant rings
We consider the problem of outputting succinct encodings of lists of gen...
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A priori generalization error for twolayer ReLU neural network through minimum norm solution
We focus on estimating a priori generalization error of twolayer ReLU n...
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Implicit bias with RitzGalerkin method in understanding deep learning for solving PDEs
This paper aims at studying the difference between RitzGalerkin (RG) m...
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Geometric rank of tensors and subrank of matrix multiplication
Motivated by problems in algebraic complexity theory (e.g., matrix multi...
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Barriers for rectangular matrix multiplication
We study the algorithmic problem of multiplying large matrices that are ...
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A comparison of group testing architectures for COVID19 testing
An important component of every country's COVID19 response is fast and ...
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The critical locus of overparameterized neural networks
Many aspects of the geometry of loss functions in deep learning remain m...
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The uncertainty principle: variations on a theme
We show how a number of wellknown uncertainty principles for the Fourie...
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Institute for Advanced Study
The IAS is perhaps best known as the academic home of Albert Einstein, Hermann Weyl, John von Neumann and Kurt Gödel, after their immigration to the United States.