6 citations · 20 across the 7 of their papers we have counts for
4 papers · 1 filter
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
Siddarth Srinivasan, Sandesh Adhikary, Jacob Miller +2
Modeling joint probability distributions over sequences has been studied from many perspectives. The physics community developed matrix product states, a tensor-train decomposition…
A Fully Tensorized Recurrent Neural Network
Charles C. Onu, Jacob E. Miller, Doina Precup
Recurrent neural networks (RNNs) are powerful tools for sequential modeling, but typically require significant overparameterization and regularization to achieve optimal performanc…
Adaptive Learning of Tensor Network Structures
Meraj Hashemizadeh, Michelle Liu, Jacob Miller +1
Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potential for machine learning applications and…
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller, Guillaume Rabusseau, John Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied within machine learning. In this work we util…