A Continuous Optimisation Benchmark Suite from Neural Network Regression

• URL: http://arxiv.org/abs/2109.05606v2
• Date: Sat, 3 Sep 2022 19:12:59 GMT
• Title: A Continuous Optimisation Benchmark Suite from Neural Network Regression
• Authors: Katherine M. Malan and Christopher W. Cleghorn
• Abstract summary: Training neural networks is an optimisation task that has gained prominence with the recent successes of deep learning. gradient descent variants are by far the most common choice with their trusted good performance on large-scale machine learning tasks. We contribute CORNN, a suite for benchmarking the performance of any continuous black-box algorithm on neural network training problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems. Training neural networks is an optimisation task that has gained prominence with the recent successes of deep learning. Although evolutionary algorithms have been used for training neural networks, gradient descent variants are by far the most common choice with their trusted good performance on large-scale machine learning tasks. With this paper we contribute CORNN (Continuous Optimisation of Regression tasks using Neural Networks), a large suite for benchmarking the performance of any continuous black-box algorithm on neural network training problems. Using a range of regression problems and neural network architectures, problem instances with different dimensions and levels of difficulty can be created. We demonstrate the use of the CORNN Suite by comparing the performance of three evolutionary and swarm-based algorithms on over 300 problem instances, showing evidence of performance complementarity between the algorithms. As a baseline, the performance of the best population-based algorithm is benchmarked against a gradient-based approach. The CORNN suite is shared as a public web repository to facilitate easy integration with existing benchmarking platforms.

Related papers

• Training Artificial Neural Networks by Coordinate Search Algorithm [0.20971479389679332]
  We propose an efficient version of the gradient-free Coordinate Search (CS) algorithm for training neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem.
  arXiv  Detail & Related papers  (2024-02-20T01:47:25Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks [51.8723187709964]
  We study the OOD generalization of neural algorithmic reasoning tasks. The goal is to learn an algorithm from input-output pairs using deep neural networks.
  arXiv  Detail & Related papers  (2022-11-01T18:33:20Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• Training Neural Networks using SAT solvers [1.0152838128195465]
  We propose an algorithm to explore the global optimisation method, using SAT solvers, for training a neural net. In the experiments, we demonstrate the effectiveness of our algorithm against the ADAM optimiser in certain tasks like parity learning.
  arXiv  Detail & Related papers  (2022-06-10T01:31:12Z)
• Neural Combinatorial Optimization: a New Player in the Field [69.23334811890919]
  This paper presents a critical analysis on the incorporation of algorithms based on neural networks into the classical optimization framework. A comprehensive study is carried out to analyse the fundamental aspects of such algorithms, including performance, transferability, computational cost and to larger-sized instances.
  arXiv  Detail & Related papers  (2022-05-03T07:54:56Z)
• Analytically Tractable Inference in Deep Neural Networks [0.0]
  Tractable Approximate Inference (TAGI) algorithm was shown to be a viable and scalable alternative to backpropagation for shallow fully-connected neural networks. We are demonstrating how TAGI matches or exceeds the performance of backpropagation, for training classic deep neural network architectures.
  arXiv  Detail & Related papers  (2021-03-09T14:51:34Z)
• On the performance of deep learning for numerical optimization: an application to protein structure prediction [0.0]
  We present a study on the performance of the deep learning models to deal with global optimization problems. The proposed approach adopts the idea of the neural architecture search (NAS) to generate efficient neural networks. Experiments reveal that the generated learning models can achieve competitive results when compared to hand-designed algorithms.
  arXiv  Detail & Related papers  (2020-12-17T17:01:30Z)
• ODEN: A Framework to Solve Ordinary Differential Equations using Artificial Neural Networks [0.0]
  We prove a specific loss function, which does not require knowledge of the exact solution, to evaluate neural networks' performance. Neural networks are shown to be proficient at approximating continuous solutions within their training domains. A user-friendly and adaptable open-source code (ODE$mathcalN$) is provided on GitHub.
  arXiv  Detail & Related papers  (2020-05-28T15:34:10Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)
• Learning to Hash with Graph Neural Networks for Recommender Systems [103.82479899868191]
  Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. We propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
  arXiv  Detail & Related papers  (2020-03-04T06:59:56Z)

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.