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
Abstract (translated)
将先前经验中的知识转移到新任务的能力是智能代理（包括人类和计算模型）的一个关键能力。这一原则构成了迁移学习的基础，在这种学习中，大型预训练神经网络被微调以适应下游任务。迁移学习在任务适应速度和性能方面都取得了巨大的成功。然而，在某些领域，由于数据缺乏，训练如此大规模的预训练模型或基础模型是不可能的——计算化学、计算免疫学和医学成像就是例子。为了解决这些挑战，我们的工作重点是设计架构，以便在大量数据不可用的情况下高效地获取先验知识。特别地，我们展示了如何使用神经记忆来适应非平稳分布，并且仅需少量样本即可实现这一目标。然后，我们证明了当使用模型无关元学习（MAML）训练时，我们的超网络设计（生成另一个网络的网络）可以比标准网络获得更通用的先验知识。随后，我们将超网络应用于三维场景生成，展示了它们只需少量训练场景就能高效地获取先验知识，从而加快从文本到3D生成的速度。最后，我们重新利用现有的分子生成方法作为预训练框架，以改善分子性质预测，解决了计算免疫学中的关键挑战。
URL
https://arxiv.org/abs/2507.10446
