Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training

• URL: http://arxiv.org/abs/2403.12320v1
• Date: Mon, 18 Mar 2024 23:23:50 GMT
• Title: Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
• Authors: Zeliang Zhang, Jinyang Jiang, Zhuo Liu, Susan Liang, Yijie Peng, Chenliang Xu,
• Abstract summary: We introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation. Experiments demonstrate the effectiveness of the approximation technique in neural network training.
• Score: 30.452060061499523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient and biologically plausible alternatives to backpropagation in neural network training remain a challenge due to issues such as high computational complexity and additional assumptions about neural networks, which limit scalability to deeper networks. The likelihood ratio method offers a promising gradient estimation strategy but is constrained by significant memory consumption, especially when deploying multiple copies of data to reduce estimation variance. In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation. By exploiting the natural parallelism during the backward pass using LR, we further provide a high-performance training strategy, which pipelines both the forward and backward pass, to make it more suitable for the computation on specialized hardware. Extensive experiments demonstrate the effectiveness of the approximation technique in neural network training. This work underscores the potential of the likelihood ratio method in achieving high-performance neural network training, suggesting avenues for further exploration.

Related papers

• Gradient-Free Training of Recurrent Neural Networks using Random Perturbations [1.1742364055094265]
  Recurrent neural networks (RNNs) hold immense potential for computations due to their Turing completeness and sequential processing capabilities. Backpropagation through time (BPTT), the prevailing method, extends the backpropagation algorithm by unrolling the RNN over time. BPTT suffers from significant drawbacks, including the need to interleave forward and backward phases and store exact gradient information. We present a new approach to perturbation-based learning in RNNs whose performance is competitive with BPTT.
  arXiv  Detail & Related papers  (2024-05-14T21:15:29Z)
• A Novel Method for improving accuracy in neural network by reinstating traditional back propagation technique [0.0]
  We propose a novel instant parameter update methodology that eliminates the need for computing gradients at each layer. Our approach accelerates learning, avoids the vanishing gradient problem, and outperforms state-of-the-art methods on benchmark data sets.
  arXiv  Detail & Related papers  (2023-08-09T16:41:00Z)
• Pruning a neural network using Bayesian inference [1.776746672434207]
  Neural network pruning is a highly effective technique aimed at reducing the computational and memory demands of large neural networks. We present a novel approach to pruning neural networks utilizing Bayesian inference, which can seamlessly integrate into the training procedure.
  arXiv  Detail & Related papers  (2023-08-04T16:34:06Z)
• One Forward is Enough for Neural Network Training via Likelihood Ratio Method [47.013384887197454]
  Backpropagation (BP) is the mainstream approach for gradient computation in neural network training. We develop a unified likelihood ratio (ULR) method for estimation with just one forward propagation.
  arXiv  Detail & Related papers  (2023-05-15T19:02:46Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Scalable computation of prediction intervals for neural networks via matrix sketching [79.44177623781043]
  Existing algorithms for uncertainty estimation require modifying the model architecture and training procedure. This work proposes a new algorithm that can be applied to a given trained neural network and produces approximate prediction intervals.
  arXiv  Detail & Related papers  (2022-05-06T13:18:31Z)
• 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)
• Parallelization Techniques for Verifying Neural Networks [52.917845265248744]
  We introduce an algorithm based on the verification problem in an iterative manner and explore two partitioning strategies. We also introduce a highly parallelizable pre-processing algorithm that uses the neuron activation phases to simplify the neural network verification problems.
  arXiv  Detail & Related papers  (2020-04-17T20:21:47Z)
• Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training [126.49572353148262]
  We study two factors in neural network training: data parallelism and sparsity. Despite their promising benefits, understanding of their effects on neural network training remains elusive.
  arXiv  Detail & Related papers  (2020-03-25T10:49:22Z)
• Large Batch Training Does Not Need Warmup [111.07680619360528]
  Training deep neural networks using a large batch size has shown promising results and benefits many real-world applications. In this paper, we propose a novel Complete Layer-wise Adaptive Rate Scaling (CLARS) algorithm for large-batch training. Based on our analysis, we bridge the gap and illustrate the theoretical insights for three popular large-batch training techniques.
  arXiv  Detail & Related papers  (2020-02-04T23:03:12Z)

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.