Optimal Network Compression
- URL: http://arxiv.org/abs/2008.08733v5
- Date: Wed, 13 Jul 2022 06:34:19 GMT
- Title: Optimal Network Compression
- Authors: Hamed Amini and Zachary Feinstein
- Abstract summary: This paper introduces a formulation of the optimal network compression problem for financial systems.
We focus on objective functions generated by systemic risk measures under shocks to the financial network.
We conclude by studying the optimal compression problem for specific networks.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper introduces a formulation of the optimal network compression
problem for financial systems. This general formulation is presented for
different levels of network compression or rerouting allowed from the initial
interbank network. We prove that this problem is, generically, NP-hard. We
focus on objective functions generated by systemic risk measures under shocks
to the financial network. We use this framework to study the (sub)optimality of
the maximally compressed network. We conclude by studying the optimal
compression problem for specific networks; this permits us to study, e.g., the
so-called robust fragility of certain network topologies more generally as well
as the potential benefits and costs of network compression. In particular,
under systematic shocks and heterogeneous financial networks the robust
fragility results of Acemoglu et al. (2015) no longer hold generally.
Related papers
- Rank Diminishing in Deep Neural Networks [71.03777954670323]
Rank of neural networks measures information flowing across layers.
It is an instance of a key structural condition that applies across broad domains of machine learning.
For neural networks, however, the intrinsic mechanism that yields low-rank structures remains vague and unclear.
arXiv Detail & Related papers (2022-06-13T12:03:32Z) - Neural Network Compression via Effective Filter Analysis and
Hierarchical Pruning [41.19516938181544]
Current network compression methods have two open problems: first, there lacks a theoretical framework to estimate the maximum compression rate; second, some layers may get over-prunned, resulting in significant network performance drop.
This study propose a gradient-matrix singularity analysis-based method to estimate the maximum network redundancy.
Guided by that maximum rate, a novel and efficient hierarchical network pruning algorithm is developed to maximally condense the neuronal network structure without sacrificing network performance.
arXiv Detail & Related papers (2022-06-07T21:30:47Z) - Fast Conditional Network Compression Using Bayesian HyperNetworks [54.06346724244786]
We introduce a conditional compression problem and propose a fast framework for tackling it.
The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts.
Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.
arXiv Detail & Related papers (2022-05-13T00:28:35Z) - On the Compression of Neural Networks Using $\ell_0$-Norm Regularization
and Weight Pruning [0.9821874476902968]
The present paper is dedicated to the development of a novel compression scheme for neural networks.
A new form of regularization is firstly developed, which is capable of inducing strong sparseness in the network during training.
The proposed compression scheme also involves the use of $ell$-norm regularization to avoid overfitting as well as fine tuning to improve the performance of the pruned network.
arXiv Detail & Related papers (2021-09-10T19:19:42Z) - Compressing Neural Networks: Towards Determining the Optimal Layer-wise
Decomposition [62.41259783906452]
We present a novel global compression framework for deep neural networks.
It automatically analyzes each layer to identify the optimal per-layer compression ratio.
Our results open up new avenues for future research into the global performance-size trade-offs of modern neural networks.
arXiv Detail & Related papers (2021-07-23T20:01:30Z) - Heavy Tails in SGD and Compressibility of Overparametrized Neural
Networks [9.554646174100123]
We show that the dynamics of the gradient descent training algorithm has a key role in obtaining compressible networks.
We prove that the networks are guaranteed to be '$ell_p$-compressible', and the compression errors of different pruning techniques become arbitrarily small as the network size increases.
arXiv Detail & Related papers (2021-06-07T17:02:59Z) - Convolutional Neural Network Pruning with Structural Redundancy
Reduction [11.381864384054824]
We claim that identifying structural redundancy plays a more essential role than finding unimportant filters.
We propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy.
arXiv Detail & Related papers (2021-04-08T00:16:24Z) - Attribution Preservation in Network Compression for Reliable Network
Interpretation [81.84564694303397]
Neural networks embedded in safety-sensitive applications rely on input attribution for hindsight analysis and network compression to reduce its size for edge-computing.
We show that these seemingly unrelated techniques conflict with each other as network compression deforms the produced attributions.
This phenomenon arises due to the fact that conventional network compression methods only preserve the predictions of the network while ignoring the quality of the attributions.
arXiv Detail & Related papers (2020-10-28T16:02:31Z) - Structured Sparsification with Joint Optimization of Group Convolution
and Channel Shuffle [117.95823660228537]
We propose a novel structured sparsification method for efficient network compression.
The proposed method automatically induces structured sparsity on the convolutional weights.
We also address the problem of inter-group communication with a learnable channel shuffle mechanism.
arXiv Detail & Related papers (2020-02-19T12:03:10Z) - Understanding Generalization in Deep Learning via Tensor Methods [53.808840694241]
We advance the understanding of the relations between the network's architecture and its generalizability from the compression perspective.
We propose a series of intuitive, data-dependent and easily-measurable properties that tightly characterize the compressibility and generalizability of neural networks.
arXiv Detail & Related papers (2020-01-14T22:26:57Z)
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.