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Found 10 papers, 4 papers with code
Energy Regularized RNNs for Solving Non-Stationary Bandit Problems
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts.
Gradient Adjusting Networks for Domain Inversion
However, in order to manipulate a real-world image, one first needs to be able to retrieve its corresponding latent representation in StyleGAN's latent space that is decoded to an image as close as possible to the desired image.
Semi-supervised Learning of Partial Differential Operators and Dynamical Flows
As a result, it successfully propagates initial conditions in continuous time steps by employing the general composition properties of the partial differential operators.
Unsupervised Disentanglement with Tensor Product Representations on the Torus
This way, the obtained representations are naturally disentangled.
Natural Statistics of Network Activations and Implications for Knowledge Distillation
As we show, these statistics, similar to image statistics, follow a power law.
Solving Non-Stationary Bandit Problems with an RNN and an Energy Minimization Loss
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts.
Shuffling Recurrent Neural Networks
We propose a novel recurrent neural network model, where the hidden state $h_t$ is obtained by permuting the vector elements of the previous hidden state $h_{t-1}$ and adding the output of a learned function $b(x_t)$ of the input $x_t$ at time $t$.
A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan.
Electric Analog Circuit Design with Hypernetworks and a Differential Simulator
A two-stage network is used, which first generates a chain of circuit components and then predicts their parameters.
Text Segmentation as a Supervised Learning Task
Text segmentation, the task of dividing a document into contiguous segments based on its semantic structure, is a longstanding challenge in language understanding.
