Transfer learning is a cornerstone of modern deep learning, yet it remains constrained by challenges in model selection and the overhead of extensive model storage. In this work, we present Diffusion-based Neural Network Weights Generation, D2NWG, a novel framework that leverages diffusion processes to synthesize task-specific network weights. By modeling the distribution of weights from a diverse ensemble of pretrained models and conditioning the generation process on dataset characteristics, task descriptions, and architectural specifications, D2NWG circumvents the need for storing and searching through massive model repositories. We evaluate D2NWG across multiple experimental settings. On in-distribution tasks, our framework achieves performance that is on par with or superior to conventional pretrained models, while also serving as an effective initialization strategy for novel domains, resulting in faster convergence and a 6\% improvement in few-shot learning scenarios. Extensive ablation studies further indicate that our approach scales robustly with increased diversity and volume of pretrained models. Moreover, D2NWG demonstrates significant promise for large language model applications. In evaluations on the OpenLM leaderboard, our method improved LLaMA-3-2-1B-Instruct performance by 3\% on challenging mathematical reasoning tasks, with a consistent gain of 0.36\% across a range of benchmarks. These findings establish D2NWG as a versatile and powerful framework for neural network weight generation, offering a scalable solution to the limitations of traditional transfer learning.

We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters. Our approach is capable of encoding neural networks in a model zoo of mixed architecture and different parameter sizes as opposed to previous approaches that require custom encoding models for different architectures. Furthermore, our \textbf{S}et-based \textbf{N}eural network \textbf{E}ncoder (SNE) takes into consideration the hierarchical computational structure of neural networks. To respect symmetries inherent in network weight space, we utilize Logit Invariance to learn the required minimal invariance properties. Additionally, we introduce a \textit{pad-chunk-encode} pipeline to efficiently encode neural network layers that is adjustable to computational and memory constraints. We also introduce two new tasks for neural network property prediction: cross-dataset and cross-architecture. In cross-dataset property prediction, we evaluate how well property predictors generalize across model zoos trained on different datasets but of the same architecture. In cross-architecture property prediction, we evaluate how well property predictors transfer to model zoos of different architecture not seen during training. We show that SNE outperforms the relevant baselines on standard benchmarks.

An indoor fingerprinting localization algorithm based on a single Artificial Neural Network (ANN) model may be subject to the Received Signal Strength Indicator (RSSI) fluctuation than multiple neural networks based fingerprinting algorithm. To date, there has not been an adequate analytical study that has investigated the performance comparison of both models. In this work, a multiple neural network fingerprinting localization model is proposed which predicts the estimated position using the result of the combination of several single neural networks. Additionally, a K-nearer coarse localizer is used to perform the position estimation task. This model has been evaluated by comparison with the existing K-Nearer Neighbor (KNN) based method and single neural network based fingerprinting localization method on a corridor and Office area and on publicly available RSSI database. The results of the proposed model are more accurate than those with KNN and single neural network model. We believe that the multiple neural networks model will be more robust for positioning algorithms using several types of features data at the same time.