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Found 8 papers, 1 papers with code
Auctions and Peer Prediction for Academic Peer Review
The revenue raised in the submission stage auction is used to pay reviewers based on the quality of their reviews in the reviewing stage.
Quantum Tensor Networks, Stochastic Processes, and Weighted Automata
We address this gap by showing how stationary or uniform versions of popular quantum tensor network models have equivalent representations in the stochastic processes and weighted automata literature, in the limit of infinitely long sequences.
Learning Deep Features in Instrumental Variable Regression
We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear.
Expressiveness and Learning of Hidden Quantum Markov Models
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in the development of hidden quantum Markov models (HQMMs) to model stochastic processes.
Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
Quantum graphical models (QGMs) extend the classical framework for reasoning about uncertainty by incorporating the quantum mechanical view of probability.
Learning and Inference in Hilbert Space with Quantum Graphical Models
Unlike classical graphical models, QGMs represent uncertainty with density matrices in complex Hilbert spaces.
A Simple and Effective Approach to the Story Cloze Test
In the Story Cloze Test, a system is presented with a 4-sentence prompt to a story, and must determine which one of two potential endings is the 'right' ending to the story.
Ranked #15 on
Question Answering
on StoryCloze
Learning Hidden Quantum Markov Models
We extend previous work on HQMMs with three contributions: (1) we show how classical hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum circuits, and (3) we present a learning algorithm to estimate the parameters of an HQMM from data.
