Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures

• URL: http://arxiv.org/abs/2507.10446v2
• Date: Tue, 15 Jul 2025 02:06:07 GMT
• Title: Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures
• Authors: Sudarshan Babu,
• Abstract summary: We focus on designing architectures to enable efficient acquisition of priors when large amounts of data are unavailable.<n>We demonstrate that our hypernetwork designs can acquire more generalizable priors than standard networks when trained with Model Agnostic Meta-Learning (MAML)<n>We extend our hypernetwork framework to perform 3D segmentation on novel scenes with limited data by efficiently transferring priors from earlier viewed scenes.
• Score: 2.5382095320488673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to transfer knowledge from prior experiences to novel tasks stands as a pivotal capability of intelligent agents, including both humans and computational models. This principle forms the basis of transfer learning, where large pre-trained neural networks are fine-tuned to adapt to downstream tasks. Transfer learning has demonstrated tremendous success, both in terms of task adaptation speed and performance. However there are several domains where, due to lack of data, training such large pre-trained models or foundational models is not a possibility - computational chemistry, computational immunology, and medical imaging are examples. To address these challenges, our work focuses on designing architectures to enable efficient acquisition of priors when large amounts of data are unavailable. In particular, we demonstrate that we can use neural memory to enable adaptation on non-stationary distributions with only a few samples. Then we demonstrate that our hypernetwork designs (a network that generates another network) can acquire more generalizable priors than standard networks when trained with Model Agnostic Meta-Learning (MAML). Subsequently, we apply hypernetworks to 3D scene generation, demonstrating that they can acquire priors efficiently on just a handful of training scenes, thereby leading to faster text-to-3D generation. We then extend our hypernetwork framework to perform 3D segmentation on novel scenes with limited data by efficiently transferring priors from earlier viewed scenes. Finally, we repurpose an existing molecular generative method as a pre-training framework that facilitates improved molecular property prediction, addressing critical challenges in computational immunology.

Related papers

• Specialized Foundation Models Struggle to Beat Supervised Baselines [60.23386520331143]
  We look at three modalities -- genomics, satellite imaging, and time series -- with multiple recent FMs and compare them to a standard supervised learning workflow.<n>We find that it is consistently possible to train simple supervised models that match or even outperform the latest foundation models.
  arXiv  Detail & Related papers  (2024-11-05T04:10:59Z)
• Towards Scalable and Versatile Weight Space Learning [51.78426981947659]
  This paper introduces the SANE approach to weight-space learning. Our method extends the idea of hyper-representations towards sequential processing of subsets of neural network weights.
  arXiv  Detail & Related papers  (2024-06-14T13:12:07Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• A Survey on Knowledge Editing of Neural Networks [43.813073385305806]
  Even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world progresses in time. Knowledge editing is emerging as a novel area of research that aims to enable reliable, data-efficient, and fast changes to a pre-trained target model. We first introduce the problem of editing neural networks, formalize it in a common framework and differentiate it from more notorious branches of research such as continuous learning.
  arXiv  Detail & Related papers  (2023-10-30T16:29:47Z)
• Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability [53.27240222619834]
  Knowledge Distillation as Efficient Pre-training aims to efficiently transfer the learned feature representation from pre-trained models to new student models for future downstream tasks. Our method performs comparably with supervised pre-training counterparts in 3 downstream tasks and 9 downstream datasets requiring 10x less data and 5x less pre-training time.
  arXiv  Detail & Related papers  (2022-03-10T06:23:41Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)
• Exploring Flip Flop memories and beyond: training recurrent neural networks with key insights [0.0]
  We study the implementation of a temporal processing task, specifically a 3-bit Flip Flop memory. The obtained networks are meticulously analyzed to elucidate dynamics, aided by an array of visualization and analysis tools.
  arXiv  Detail & Related papers  (2020-10-15T16:25:29Z)
• Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification [53.735029033681435]
  Transfer learning is a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. In this work, we demonstrate that adversarially-trained models transfer better than non-adversarially-trained models.
  arXiv  Detail & Related papers  (2020-07-11T22:48:42Z)
• A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks [1.1802674324027231]
  Self-supervised pre-training for transfer learning is becoming an increasingly popular technique to improve state-of-the-art results using unlabeled data. We provide an overview of the taxonomy for self-supervised learning and transfer learning, and highlight some prominent methods for designing pre-training tasks across different domains.
  arXiv  Detail & Related papers  (2020-07-01T22:55:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.