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Found 10 papers, 1 papers with code
Active Learning of Quantum System Hamiltonians yields Query Advantage
Hamiltonian learning is an important procedure in quantum system identification, calibration, and successful operation of quantum computers.
Dither computing: a hybrid deterministic-stochastic computing framework
We proposed an alternative framework, called dither computing, that combines aspects of stochastic computing and its deterministic variants and that can perform computing with similar efficiency, is unbiased, and with a variance and MSE also on the optimal order of $\Theta(\frac{1}{N^2})$.
Synchronization in dynamical systems coupled via multiple directed networks
We study synchronization and consensus in a group of dynamical systems coupled via multiple directed networks.
A Family of Robust Stochastic Operators for Reinforcement Learning
We consider a new family of stochastic operators for reinforcement learning with the goal of alleviating negative effects and becoming more robust to approximation or estimation errors.
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities.
TableNet: a multiplier-less implementation of neural networks for inferencing
We consider the use of look-up tables (LUT) to simplify the hardware implementation of a deep learning network for inferencing after weights have been successfully trained.
ProdSumNet: reducing model parameters in deep neural networks via product-of-sums matrix decompositions
We show that good accuracy on MNIST and Fashion MNIST can be obtained using a relatively small number of trainable parameters.
A General Family of Robust Stochastic Operators for Reinforcement Learning
We consider a new family of operators for reinforcement learning with the goal of alleviating the negative effects and becoming more robust to approximation or estimation errors.
Designing communication systems via iterative improvement: error correction coding with Bayes decoder and codebook optimized for source symbol error
For this metric, the positions of the bits are not relevant to the decoding, and in many noise models, not relevant to the BER either.
Can machine learning identify interesting mathematics? An exploration using empirically observed laws
We explore the possibility of using machine learning to identify interesting mathematical structures by using certain quantities that serve as fingerprints.
