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07/03/2023
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
We present the Temporal Graph Benchmark (TGB), a collection of challengi...
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05/15/2023
Fast and Attributed Change Detection on Dynamic Graphs with Density of States
How can we detect traffic disturbances from international flight transpo...
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02/02/2023
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs
Dynamic graphs are rich data structures that are used to model complex r...
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11/04/2022
Spectral Regularization: an Inductive Bias for Sequence Modeling
Various forms of regularization in learning tasks strive for different n...
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06/08/2022
Sequential Density Estimation via NCWFAs Sequential Density Estimation via Nonlinear Continuous Weighted Finite Automata
Weighted finite automata (WFAs) have been widely applied in many fields....
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05/24/2022
High-Order Pooling for Graph Neural Networks with Tensor Decomposition
Graph Neural Networks (GNNs) are attracting growing attention due to the...
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10/26/2021
Rademacher Random Projections with Tensor Networks
Random projection (RP) have recently emerged as popular techniques in th...
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06/22/2021
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models
Tensor network methods have been a key ingredient of advances in condens...
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06/05/2021
Extracting Weighted Automata for Approximate Minimization in Language Modelling
In this paper we study the approximate minimization problem for language...
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01/13/2021
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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10/20/2020
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Modeling joint probability distributions over sequences has been studied...
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10/19/2020
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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10/07/2020
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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08/12/2020
Adaptive Tensor Learning with Tensor Networks
Tensor decomposition techniques have shown great successes in machine le...
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07/02/2020
Laplacian Change Point Detection for Dynamic Graphs
Dynamic and temporal graphs are rich data structures that are used to mo...
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03/11/2020
Tensorized Random Projections
We introduce a novel random projection technique for efficiently reducin...
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03/02/2020
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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03/02/2020
Tensor Networks for Language Modeling
The tensor network formalism has enjoyed over two decades of success in ...
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02/07/2020
Provably efficient reconstruction of policy networks
Recent research has shown that learning poli-cies parametrized by large ...
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11/12/2019
Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning
Learning and planning in partially-observable domains is one of the most...
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09/14/2019
Neural Architecture Search for Class-incremental Learning
In class-incremental learning, a model learns continuously from a sequen...
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12/18/2018
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
The standard loss function used to train neural network classifiers, cat...
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10/17/2018
Hierarchical Methods of Moments
Spectral methods of moments provide a powerful tool for learning the par...
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09/13/2018
Sequential Coordination of Deep Models for Learning Visual Arithmetic
Achieving machine intelligence requires a smooth integration of percepti...
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07/04/2018
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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06/21/2018
Learning Graph Weighted Models on Pictures
Graph Weighted Models (GWMs) have recently been proposed as a natural ge...
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12/27/2017
Tensor Regression Networks with various Low-Rank Tensor Approximations
Tensor regression networks achieve high rate of compression of model par...
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09/13/2017
