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HyperNetworks - OpenReview Abstract: This work explores hypernetworks: an approach of using one network, also known as a hypernetwork, to generate the weights for another network We apply hypernetworks to generate adaptive weights for recurrent networks In this case, hypernetworks can be viewed as a relaxed form of weight-sharing across layers
HYPERNETWORKS - OpenReview hypernetworks can be viewed as a relaxed form of weight-sharing across layers In our implementation, hypernetworks are are trained jointly with the main net-work in an end-to-end fashion Our main result is that hypernetworks can gener-ate non-shared weights for LSTM and achieve state-of-the-art results on a variety
SMASH: One-Shot Model Architecture Search through HyperNetworks SMASH: One-Shot Model Architecture Search through HyperNetworks Andrew Brock , Theo Lim , J M Ritchie , Nick Weston 15 Feb 2018 (modified: 13 Apr 2025) ICLR 2018 Conference Blind Submission Readers: Everyone
HAIR: HYPERNETWORKS BASED ALL IN-ONE IM AGE RESTORATION - OpenReview each task To alleviate this issue, we propose HAIR, a Hypernetworks-based All-in-One Image Restoration plug-and-play method that generates parameters based on the input image and thus makes the model to adapt to specific degradation dy-namically Specifically, HAIR consists of two main components, i e , Classifier and Hyper Selecting Net (HSN)
Bayesian Hypernetworks - OpenReview Abstract: We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network)
P WEIGHT INITIALIZATION FOR HYPERNETWORKS - OpenReview apply directly to hypernetworks and novel ways of thinking about weight initialization, optimization dynamics and architecture design for hypernetworks are sorely needed 2 1 RICCI CALCULUS We propose the use of Ricci calculus, as opposed to the more commonly used matrix calculus, as a suitable mathematical language for thinking about
Breaking Long-Tailed Learning Bottlenecks: A Controllable . . . - OpenReview We generate a set of diverse expert models via hypernetworks to cover all possible distribution scenarios, and optimize the model ensemble to adapt to any test distribution Crucially, in any distribution scenario, we can flexibly output a dedicated model solution that matches the user's preference
MotherNet: Fast Training and Inference via Hyper-Network Transformers In contrast to most existing hypernetworks that are usually trained for relatively constrained multi-task settings, \MotherNet can create models for multiclass classification on arbitrary tabular datasets without any dataset specific gradient descent
LEARNING THE PARETO FRONT HYPERNETWORKS - OpenReview that implements this idea using HyperNetworks Specifically, we train a hypernetwork, termed Pareto Hypernetwork (PHN), that given a preference vector as an input, produces a deep network model tuned for that objective preference Training is applied to preferences sampled from the m dimensional simplex where mrepresents the number of objectives
HYPERNETWORK APPROACH TO BAYESIAN MAML - OpenReview algorithm works Finally, we introduce general idea of Hypernetworks dedicated for MAML updates The terminology describing the Few-Shot learning setup is dispersive due to the colliding defini-tions used in the literature Here, we use the nomenclature derived from the Meta-Learning literature, which is the most prevalent at the time of writing