Testing RadiX-Nets: Advances in Viable Sparse Topologies

• URL: http://arxiv.org/abs/2311.03609v1
• Date: Mon, 6 Nov 2023 23:27:28 GMT
• Title: Testing RadiX-Nets: Advances in Viable Sparse Topologies
• Authors: Kevin Kwak, Zack West, Hayden Jananthan, Jeremy Kepner
• Abstract summary: Sparsification of hyper-parametrized deep neural networks (DNNs) creates simpler representations of complex data. RadiX-Nets, a subgroup of DNNs, maintain runtime which counteracts their lack of neural connections. This paper presents a testing suite for RadiX-Nets in scalable models.
• Score: 0.9555447998395205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exponential growth of data has sparked computational demands on ML research and industry use. Sparsification of hyper-parametrized deep neural networks (DNNs) creates simpler representations of complex data. Past research has shown that some sparse networks achieve similar performance as dense ones, reducing runtime and storage. RadiX-Nets, a subgroup of sparse DNNs, maintain uniformity which counteracts their lack of neural connections. Generation, independent of a dense network, yields faster asymptotic training and removes the need for costly pruning. However, little work has been done on RadiX-Nets, making testing challenging. This paper presents a testing suite for RadiX-Nets in TensorFlow. We test RadiX-Net performance to streamline processing in scalable models, revealing relationships between network topology, initialization, and training behavior. We also encounter "strange models" that train inconsistently and to lower accuracy while models of similar sparsity train well.

Related papers

• Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight Pruning [14.792099973449794]
  We propose an algorithm to align the training dynamics of the sparse network with that of the dense one. We show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account. Path eXclusion (PX) is able to find lottery tickets even at high sparsity levels.
  arXiv  Detail & Related papers  (2024-06-03T22:19:42Z)
• On Generalization Bounds for Deep Compound Gaussian Neural Networks [1.4425878137951238]
  Unrolled deep neural networks (DNNs) provide better interpretability and superior empirical performance than standard DNNs. We develop novel generalization error bounds for a class of unrolled DNNs informed by a compound Gaussian prior. Under realistic conditions, we show that, at worst, the generalization error scales $mathcalO(nsqrt(n))$ in the signal dimension and $mathcalO(($Network Size$)3/2)$ in network size.
  arXiv  Detail & Related papers  (2024-02-20T16:01:39Z)
• Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks [89.28881869440433]
  This paper provides the first theoretical characterization of joint edge-model sparse learning for graph neural networks (GNNs) It proves analytically that both sampling important nodes and pruning neurons with the lowest-magnitude can reduce the sample complexity and improve convergence without compromising the test accuracy.
  arXiv  Detail & Related papers  (2023-02-06T16:54:20Z)
• 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)
• Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns [77.34726150561087]
  We introduce four complicated missing patterns, including missing and three fiber-like missing cases according to the mode-drivenn fibers. Despite nonity of the objective function in our model, we derive the optimal solutions by integrating alternating data-mputation method of multipliers.
  arXiv  Detail & Related papers  (2022-05-19T08:37:56Z)
• Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks [79.74580058178594]
  We analyze the performance of training a pruned neural network by analyzing the geometric structure of the objective function. We show that the convex region near a desirable model with guaranteed generalization enlarges as the neural network model is pruned.
  arXiv  Detail & Related papers  (2021-10-12T01:11:07Z)
• SpikeMS: Deep Spiking Neural Network for Motion Segmentation [7.491944503744111]
  textitSpikeMS is the first deep encoder-decoder SNN architecture for the real-world large-scale problem of motion segmentation. We show that textitSpikeMS is capable of textitincremental predictions, or predictions from smaller amounts of test data than it is trained on.
  arXiv  Detail & Related papers  (2021-05-13T21:34:55Z)
• Online Limited Memory Neural-Linear Bandits with Likelihood Matching [53.18698496031658]
  We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. We propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
  arXiv  Detail & Related papers  (2021-02-07T14:19:07Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study [2.3605348648054463]
  This work explores the use of high performance computing with distributed training to address the challenges of training BNNs at scale. We present a performance and scalability comparison of training the VGG-16 and Resnet-18 models on a Cray-XC40 cluster.
  arXiv  Detail & Related papers  (2020-05-23T23:15:34Z)

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.