4,564 research outputs found
Pruning Deep Convolutional Neural Networks Architectures with Evolution Strategy
Currently, Deep Convolutional Neural Networks (DCNNs) are used to solve all
kinds of problems in the field of machine learning and artificial intelligence
due to their learning and adaptation capabilities. However, most successful
DCNN models have a high computational complexity making them difficult to
deploy on mobile or embedded platforms. This problem has prompted many
researchers to develop algorithms and approaches to help reduce the
computational complexity of such models. One of them is called filter pruning,
where convolution filters are eliminated to reduce the number of parameters
and, consequently, the computational complexity of the given model. In the
present work, we propose a novel algorithm to perform filter pruning by using
Multi-Objective Evolution Strategy (ES) algorithm, called DeepPruningES. Our
approach avoids the need for using any knowledge during the pruning procedure
and helps decision-makers by returning three pruned CNN models with different
trade-offs between performance and computational complexity. We show that
DeepPruningES can significantly reduce a model's computational complexity by
testing it on three DCNN architectures: Convolutional Neural Networks (CNNs),
Residual Neural Networks (ResNets), and Densely Connected Neural Networks
(DenseNets).Comment: Accepted at Information Science
Joint Multi-Dimension Pruning
We present joint multi-dimension pruning (named as JointPruning), a new
perspective of pruning a network on three crucial aspects: spatial, depth and
channel simultaneously. The joint strategy enables to search a better status
than previous studies that focused on individual dimension solely, as our
method is optimized collaboratively across the three dimensions in a single
end-to-end training. Moreover, each dimension that we consider can promote to
get better performance through colluding with the other two. Our method is
realized by the adapted stochastic gradient estimation. Extensive experiments
on large-scale ImageNet dataset across a variety of network architectures
MobileNet V1&V2 and ResNet demonstrate the effectiveness of our proposed
method. For instance, we achieve significant margins of 2.5% and 2.6%
improvement over the state-of-the-art approach on the already compact MobileNet
V1&V2 under an extremely large compression ratio
Meta-Learning of Neural Architectures for Few-Shot Learning
The recent progress in neural architecture search (NAS) has allowed scaling
the automated design of neural architectures to real-world domains, such as
object detection and semantic segmentation. However, one prerequisite for the
application of NAS are large amounts of labeled data and compute resources.
This renders its application challenging in few-shot learning scenarios, where
many related tasks need to be learned, each with limited amounts of data and
compute time. Thus, few-shot learning is typically done with a fixed neural
architecture. To improve upon this, we propose MetaNAS, the first method which
fully integrates NAS with gradient-based meta-learning. MetaNAS optimizes a
meta-architecture along with the meta-weights during meta-training. During
meta-testing, architectures can be adapted to a novel task with a few steps of
the task optimizer, that is: task adaptation becomes computationally cheap and
requires only little data per task. Moreover, MetaNAS is agnostic in that it
can be used with arbitrary model-agnostic meta-learning algorithms and
arbitrary gradient-based NAS methods. %We present encouraging results for
MetaNAS with a combination of DARTS and REPTILE on few-shot classification
benchmarks. Empirical results on standard few-shot classification benchmarks
show that MetaNAS with a combination of DARTS and REPTILE yields
state-of-the-art results
Rethinking Performance Estimation in Neural Architecture Search
Neural architecture search (NAS) remains a challenging problem, which is
attributed to the indispensable and time-consuming component of performance
estimation (PE). In this paper, we provide a novel yet systematic rethinking of
PE in a resource constrained regime, termed budgeted PE (BPE), which precisely
and effectively estimates the performance of an architecture sampled from an
architecture space. Since searching an optimal BPE is extremely time-consuming
as it requires to train a large number of networks for evaluation, we propose a
Minimum Importance Pruning (MIP) approach. Given a dataset and a BPE search
space, MIP estimates the importance of hyper-parameters using random forest and
subsequently prunes the minimum one from the next iteration. In this way, MIP
effectively prunes less important hyper-parameters to allocate more
computational resource on more important ones, thus achieving an effective
exploration. By combining BPE with various search algorithms including
reinforcement learning, evolution algorithm, random search, and differentiable
architecture search, we achieve 1, 000x of NAS speed up with a negligible
performance drop comparing to the SOT
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
Recently Neural Architecture Search (NAS) has aroused great interest in both
academia and industry, however it remains challenging because of its huge and
non-continuous search space. Instead of applying evolutionary algorithm or
reinforcement learning as previous works, this paper proposes a Direct Sparse
Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning
view to NAS problem. In specific, we start from a completely connected block,
and then introduce scaling factors to scale the information flow between
operations. Next, we impose sparse regularizations to prune useless connections
in the architecture. Lastly, we derive an efficient and theoretically sound
optimization method to solve it. Our method enjoys both advantages of
differentiability and efficiency, therefore can be directly applied to large
datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an
average test error 2.84\%, while on the ImageNet dataset DSO-NAS achieves
25.4\% test error under 600M FLOPs with 8 GPUs in 18 hours.Comment: ICLR2019 Submissio
Towards Evolutional Compression
Compressing convolutional neural networks (CNNs) is essential for
transferring the success of CNNs to a wide variety of applications to mobile
devices. In contrast to directly recognizing subtle weights or filters as
redundant in a given CNN, this paper presents an evolutionary method to
automatically eliminate redundant convolution filters. We represent each
compressed network as a binary individual of specific fitness. Then, the
population is upgraded at each evolutionary iteration using genetic operations.
As a result, an extremely compact CNN is generated using the fittest
individual. In this approach, either large or small convolution filters can be
redundant, and filters in the compressed network are more distinct. In
addition, since the number of filters in each convolutional layer is reduced,
the number of filter channels and the size of feature maps are also decreased,
naturally improving both the compression and speed-up ratios. Experiments on
benchmark deep CNN models suggest the superiority of the proposed algorithm
over the state-of-the-art compression methods
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden
Machine Learning (ML) applications on healthcare can have a great impact on
people's lives helping deliver better and timely treatment to those in need. At
the same time, medical data is usually big and sparse requiring important
computational resources. Although it might not be a problem for wide-adoption
of ML tools in developed nations, availability of computational resource can
very well be limited in third-world nations. This can prevent the less favored
people from benefiting of the advancement in ML applications for healthcare. In
this project we explored methods to increase computational efficiency of ML
algorithms, in particular Artificial Neural Nets (NN), while not compromising
the accuracy of the predicted results. We used in-hospital mortality prediction
as our case analysis based on the MIMIC III publicly available dataset. We
explored three methods on two different NN architectures. We reduced the size
of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of
"unused" neurons. Additionally, we modified the RNN structure by adding a
hidden-layer to the LSTM cell allowing to use less recurrent layers for the
model. Finally, we implemented quantization on DNN forcing the weights to be
8-bits instead of 32-bits. We found that all our methods increased
computational efficiency without compromising accuracy and some of them even
achieved higher accuracy than the pre-condensed baseline models
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning
While bigger and deeper neural network architectures continue to advance the
state-of-the-art for many computer vision tasks, real-world adoption of these
networks is impeded by hardware and speed constraints. Conventional model
compression methods attempt to address this problem by modifying the
architecture manually or using pre-defined heuristics. Since the space of all
reduced architectures is very large, modifying the architecture of a deep
neural network in this way is a difficult task. In this paper, we tackle this
issue by introducing a principled method for learning reduced network
architectures in a data-driven way using reinforcement learning. Our approach
takes a larger `teacher' network as input and outputs a compressed `student'
network derived from the `teacher' network. In the first stage of our method, a
recurrent policy network aggressively removes layers from the large `teacher'
model. In the second stage, another recurrent policy network carefully reduces
the size of each remaining layer. The resulting network is then evaluated to
obtain a reward -- a score based on the accuracy and compression of the
network. Our approach uses this reward signal with policy gradients to train
the policies to find a locally optimal student network. Our experiments show
that we can achieve compression rates of more than 10x for models such as
ResNet-34 while maintaining similar performance to the input `teacher' network.
We also present a valuable transfer learning result which shows that policies
which are pre-trained on smaller `teacher' networks can be used to rapidly
speed up training on larger `teacher' networks
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure
Model-based compression is an effective, facilitating, and expanded model of
neural network models with limited computing and low power. However,
conventional models of compression techniques utilize crafted features [2,3,12]
and explore specialized areas for exploration and design of large spaces in
terms of size, speed, and accuracy, which usually have returns Less and time is
up. This paper will effectively analyze deep auto compression (ADC) and
reinforcement learning strength in an effective sample and space design, and
improve the compression quality of the model. The results of compression of the
advanced model are obtained without any human effort and in a completely
automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher
than the manual compression model for VGG-16 in ImageNet
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is
transforming the way AI is applied in practice. Pre-trained neural network
models available through APIs or capability to custom train pre-built neural
network architectures with customer data has made the consumption of AI by
developers much simpler and resulted in broad adoption of these complex AI
models. While prebuilt network models exist for certain scenarios, to try and
meet the constraints that are unique to each application, AI teams need to
think about developing custom neural network architectures that can meet the
tradeoff between accuracy and memory footprint to achieve the tight constraints
of their unique use-cases. However, only a small proportion of data science
teams have the skills and experience needed to create a neural network from
scratch, and the demand far exceeds the supply. In this paper, we present
NeuNetS : An automated Neural Network Synthesis engine for custom neural
network design that is available as part of IBM's AI OpenScale's product.
NeuNetS is available for both Text and Image domains and can build neural
networks for specific tasks in a fraction of the time it takes today with human
effort, and with accuracy similar to that of human-designed AI models.Comment: 14 pages, 12 figures. arXiv admin note: text overlap with
arXiv:1806.0025
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