… PFL implemented using HyperNetworks, which we term Pareto HyperNetworks (PHNs).
PHN learns the entire Pareto front simultaneously using a single hypernetwork, which receives …

… based on task-conditioned hypernetworks, ie, networks that … data, task-conditioned
hypernetworks only require rehearsing task-… that task-conditioned hypernetworks display a very …

Neural networks are powerful and flexible models that work well for many difficult learning
tasks in image, speech and natural language understanding. Despite their success, neural …

… We propose Graph HyperNetwork that predicts the parameters of unseen neural networks
by directly operating on their computational graph representations. 2. Our approach achieves …

… Hypernetworks [36] suggested making the phenotype directly dependent on the inputs, thus
tailoring the weights of the phenotype to the specific inputs of the network. By incorporating …

… work (Ha et al., 2017) to generate its weight. Such usage of the hypernetwork in SMASH …
This is because the hypernetwork generates weights for SMASH’s child models via tensor …

… (2017) used hypernetworks (Ha et al., 2017) to generate the weights for a randomly sampled
network architecture with the goal of eliminating the costly process of training a vast amount …

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout
stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout …

… “projected” context by the hypernetwork; or (c) the … hypernetwork that generates a weight
matrix for a matrix multiplication. Similarly, a diagonal 3D tensor is equivalent to a hypernetwork …

… Published as a conference paper at ICLR 2017 … Published as a conference paper at ICLR
2017 … JC would also like to thank Guillaume Alain, Kyle Kastner and David Ha for providing us …