Magnitude Invariant Parametrizations Improve Hypernetwork Learning
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2304.07645
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Published
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1
Hypernetwork
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HyperShot: Few-Shot Learning by Kernel HyperNetworks
Paper • 2203.11378 • Published • 1 -
Hypernetworks for Zero-shot Transfer in Reinforcement Learning
Paper • 2211.15457 • Published • 1 -
Continual Learning with Dependency Preserving Hypernetworks
Paper • 2209.07712 • Published • 1 -
HyperMixer: An MLP-based Low Cost Alternative to Transformers
Paper • 2203.03691 • Published • 1 -
Task-Agnostic Low-Rank Adapters for Unseen English Dialects
Paper • 2311.00915 • Published • 1 -
Recomposing the Reinforcement Learning Building Blocks with Hypernetworks
Paper • 2106.06842 • Published • 1 -
Continual Model-Based Reinforcement Learning with Hypernetworks
Paper • 2009.11997 • Published • 1 -
Continual learning with hypernetworks
Paper • 1906.00695 • Published • 1 -
HyperTuning: Toward Adapting Large Language Models without Back-propagation
Paper • 2211.12485 • Published • 1 -
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
Paper • 2310.11670 • Published • 1 -
Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks
Paper • 2312.16218 • Published • 7 -
A Brief Review of Hypernetworks in Deep Learning
Paper • 2306.06955 • Published • 1 -
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
Paper • 2106.04489 • Published • 1 -
Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks
Paper • 2210.03265 • Published • 1 -
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer
Paper • 2205.12148 • Published • 2 -
Specialized Language Models with Cheap Inference from Limited Domain Data
Paper • 2402.01093 • Published • 45 -
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models
Paper • 2403.13447 • Published • 18 -
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters
Paper • 2405.16287 • Published • 10 -
HyperInterval: Hypernetwork approach to training weight interval regions in continual learning
Paper • 2405.15444 • Published