Balancing Sparse RNNs with Hyperparameterization Benefiting Meta-Learning

• URL: http://arxiv.org/abs/2509.15057v1
• Date: Thu, 18 Sep 2025 15:20:13 GMT
• Title: Balancing Sparse RNNs with Hyperparameterization Benefiting Meta-Learning
• Authors: Quincy Hershey, Randy Paffenroth,
• Abstract summary: This paper develops alternative hyper parameters for specifying sparse Recurrent Neural Networks (RNNs)<n>These hyper parameters allow for varying sparsity within the trainable weight matrices of the model while improving overall performance.<n>This architecture enables the definition of a novel metric, hidden proportion, which seeks to balance the distribution of unknowns within the model and provides significant explanatory power of model performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper develops alternative hyperparameters for specifying sparse Recurrent Neural Networks (RNNs). These hyperparameters allow for varying sparsity within the trainable weight matrices of the model while improving overall performance. This architecture enables the definition of a novel metric, hidden proportion, which seeks to balance the distribution of unknowns within the model and provides significant explanatory power of model performance. Together, the use of the varied sparsity RNN architecture combined with the hidden proportion metric generates significant performance gains while improving performance expectations on an a priori basis. This combined approach provides a path forward towards generalized meta-learning applications and model optimization based on intrinsic characteristics of the data set, including input and output dimensions.

Related papers

• Detecting and Pruning Prominent but Detrimental Neurons in Large Language Models [68.57424628540907]
  Large language models (LLMs) often develop learned mechanisms specialized to specific datasets.<n>We introduce a fine-tuning approach designed to enhance generalization by identifying and pruning neurons associated with dataset-specific mechanisms.<n>Our method employs Integrated Gradients to quantify each neuron's influence on high-confidence predictions, pinpointing those that disproportionately contribute to dataset-specific performance.
  arXiv  Detail & Related papers  (2025-07-12T08:10:10Z)
• Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization [49.567092222782435]
  We introduce the novel concept of XAI consistency, defined as the agreement among different feature attribution methods.<n>We create a multi-objective optimization framework that balances predictive performance with explanation.<n>Our research provides a foundation for future investigations into whether models from the trade-off zone-balancing performance loss and XAI consistency-exhibit greater robustness.
  arXiv  Detail & Related papers  (2025-05-12T13:19:14Z)
• Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy [104.48511402784763]
  Performance Law for SR models aims to theoretically investigate and model the relationship between model performance and data quality.<n>We propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics.
  arXiv  Detail & Related papers  (2024-11-30T10:56:30Z)
• An Efficient Approach to Regression Problems with Tensor Neural Networks [5.345144592056051]
  This paper introduces a tensor neural network (TNN) to address nonparametric regression problems. The TNN demonstrates superior performance compared to conventional Feed-Forward Networks (FFN) and Radial Basis Function Networks (RBN) A significant innovation in our approach is the integration of statistical regression and numerical integration within the TNN framework.
  arXiv  Detail & Related papers  (2024-06-14T03:38:40Z)
• Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training [32.154166415680066]
  Methods like distillation, compression or quantization help leverage the highly performant large models to induce smaller performant ones. This paper explores the hypothesis that a single training run can simultaneously train a larger model for performance and derive a smaller model for deployment.
  arXiv  Detail & Related papers  (2024-02-07T17:07:41Z)
• Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights [4.513581513983453]
  We present a first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN. This method models the optimization variable trajectory as a dynamical system and develops a discretization algorithm that adaptively selects step sizes based on the trajectory's shape.
  arXiv  Detail & Related papers  (2023-10-21T03:45:13Z)
• Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks [2.1797442801107056]
  Undirected Weighted Network (UWN) is commonly found in big data-related applications. Existing models fail in either modeling its intrinsic symmetry or low-data density. Proximal Symmetric Nonnegative Latent-factor-analysis model is proposed.
  arXiv  Detail & Related papers  (2023-06-06T13:03:24Z)
• Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture [68.13678918660872]
  We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
  arXiv  Detail & Related papers  (2023-03-27T02:34:09Z)
• Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics [6.940097162264939]
  randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality. In light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction.
  arXiv  Detail & Related papers  (2022-05-26T07:07:26Z)
• Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics [61.49826776409194]
  We analyze a corpus of models made publicly-available for a contest to predict the generalization accuracy of neural network (NN) models. We identify what amounts to a Simpson's paradox: where "scale" metrics perform well overall but perform poorly on sub partitions of the data. We present two novel shape metrics, one data-independent, and the other data-dependent, which can predict trends in the test accuracy of a series of NNs.
  arXiv  Detail & Related papers  (2021-06-01T19:19:49Z)
• A Fully Tensorized Recurrent Neural Network [48.50376453324581]
  We introduce a "fully tensorized" RNN architecture which jointly encodes the separate weight matrices within each recurrent cell. This approach reduces model size by several orders of magnitude, while still maintaining similar or better performance compared to standard RNNs.
  arXiv  Detail & Related papers  (2020-10-08T18:24:12Z)

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.