
Rademacher Random Projections with Tensor Networks
Random projection (RP) have recently emerged as popular techniques in th...
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Lower and Upper Bounds on the VCDimension of Tensor Network Models
Tensor network methods have been a key ingredient of advances in condens...
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Extracting Weighted Automata for Approximate Minimization in Language Modelling
In this paper we study the approximate minimization problem for language...
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Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application
Airlines have been making use of highly complex Revenue Management Syste...
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Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Modeling joint probability distributions over sequences has been studied...
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Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
In this paper, we present connections between three models used in diffe...
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A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix
Continual learning (CL) is a setting in which an agent has to learn from...
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Adaptive Tensor Learning with Tensor Networks
Tensor decomposition techniques have shown great successes in machine le...
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Laplacian Change Point Detection for Dynamic Graphs
Dynamic and temporal graphs are rich data structures that are used to mo...
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Tensorized Random Projections
We introduce a novel random projection technique for efficiently reducin...
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RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Almost all neural architecture search methods are evaluated in terms of ...
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Tensor Networks for Language Modeling
The tensor network formalism has enjoyed over two decades of success in ...
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Provably efficient reconstruction of policy networks
Recent research has shown that learning policies parametrized by large ...
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Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Learning and planning in partiallyobservable domains is one of the most...
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Neural Architecture Search for Classincremental Learning
In classincremental learning, a model learns continuously from a sequen...
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ClusteringOriented Representation Learning with AttractiveRepulsive Loss
The standard loss function used to train neural network classifiers, cat...
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Hierarchical Methods of Moments
Spectral methods of moments provide a powerful tool for learning the par...
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Sequential Coordination of Deep Models for Learning Visual Arithmetic
Achieving machine intelligence requires a smooth integration of percepti...
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Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning
In this paper, we unravel a fundamental connection between weighted fini...
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Learning Graph Weighted Models on Pictures
Graph Weighted Models (GWMs) have recently been proposed as a natural ge...
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Tensor Regression Networks with various LowRank Tensor Approximations
Tensor regression networks achieve high rate of compression of model par...
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Neural Network Based Nonlinear Weighted Finite Automata
Weighted finite automata (WFA) can expressively model functions defined ...
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Guillaume Rabusseau
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