Macro placement is the problem of placing memory blocks on a chip canvas.
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Quantum Field Theories.
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.
In this work we develop a quantum field theory formalism for deep learning, where input signals are encoded in Gaussian states, a generalization of Gaussian processes which encode the agent's uncertainty about the input signal.
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive cost functions on permutations.
We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states.
Continuous input signals like images and time series that are irregularly sampled or have missing values are challenging for existing deep learning methods.