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Found 7 papers, 2 papers with code
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
In particular, we propose to approximate a time-dependent operator $\mathcal V_t$ whose time integral provides a mapping between the functional distributions of the free theory $[\mathcal D\phi(x)] \mathcal Z_0^{-1} e^{-\mathcal S_{0}[\phi(x)]}$ and of the target theory $[\mathcal D\phi(x)]\mathcal Z^{-1}e^{-\mathcal S[\phi(x)]}$.
Graph Neural Networks Go Forward-Forward
We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes.
Learning Interpolations between Boltzmann Densities
We introduce a training objective for continuous normalizing flows that can be used in the absence of samples but in the presence of an energy function.
Deformations of Boltzmann Distributions
Consider a one-parameter family of Boltzmann distributions $p_t(x) = \tfrac{1}{Z_t}e^{-S_t(x)}$.
Flowification: Everything is a Normalizing Flow
On the other hand, neural networks only perform a forward pass on the input, there is neither a notion of an inverse of a neural network nor is there one of its likelihood contribution.
Beyond Ansätze: Learning Quantum Circuits as Unitary Operators
This paper explores the advantages of optimizing quantum circuits on $N$ wires as operators in the unitary group $U(2^N)$.
Speeding up PCA with priming
This algorithm first runs any approximate-PCA method to get an initial estimate of the principal components (priming), and then applies an exact PCA in the subspace they span.
