Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures

• URL: http://arxiv.org/abs/2101.06518v1
• Date: Sat, 16 Jan 2021 20:37:29 GMT
• Title: Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures
• Authors: Yigit Alparslan, Ethan Jacob Moyer, Edward Kim
• Abstract summary: In this paper, we study compact neural network architectures for binary classification. We investigate improvements in speed and accuracy when favoring overcomplete architecture candidates.
• Score: 6.123324869194195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Architecture sizes for neural networks have been studied widely and several search methods have been offered to find the best architecture size in the shortest amount of time possible. In this paper, we study compact neural network architectures for binary classification and investigate improvements in speed and accuracy when favoring overcomplete architecture candidates that have a very high-dimensional representation of the input. We hypothesize that an overcomplete model architecture that creates a relatively high-dimensional representation of the input will be not only be more accurate but would also be easier and faster to find. In an NxM search space, we propose an online traversal algorithm that finds the best architecture candidate in O(1) time for best case and O(N) amortized time for average case for any compact binary classification problem by using k-completeness as heuristics in our search. The two other offline search algorithms we implement are brute force traversal and diagonal traversal, which both find the best architecture candidate in O(NxM) time. We compare our new algorithm to brute force and diagonal searching as a baseline and report search time improvement of 52.1% over brute force and of 15.4% over diagonal search to find the most accurate neural network architecture when given the same dataset. In all cases discussed in the paper, our online traversal algorithm can find an accurate, if not better, architecture in significantly shorter amount of time.

Related papers

• Flexible Channel Dimensions for Differentiable Architecture Search [50.33956216274694]
  We propose a novel differentiable neural architecture search method with an efficient dynamic channel allocation algorithm. We show that the proposed framework is able to find DNN architectures that are equivalent to previous methods in task accuracy and inference latency.
  arXiv  Detail & Related papers  (2023-06-13T15:21:38Z)
• Learning to Hash Robustly, with Guarantees [79.68057056103014]
  In this paper, we design an NNS algorithm for the Hamming space that has worst-case guarantees essentially matching that of theoretical algorithms. We evaluate the algorithm's ability to optimize for a given dataset both theoretically and practically. Our algorithm has a 1.8x and 2.1x better recall on the worst-performing queries to the MNIST and ImageNet datasets.
  arXiv  Detail & Related papers  (2021-08-11T20:21:30Z)
• Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems [4.3871352596331255]
  In this study, we optimize the selection process by investigating different search algorithms to find a neural network architecture size that yields the highest accuracy. We apply binary search on a very well-defined binary classification network search space and compare the results to those of linear search. We report a 100-fold running time improvement over the naive linear search when we apply the binary search method to our datasets.
  arXiv  Detail & Related papers  (2021-01-16T20:00:38Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Efficient Sampling for Predictor-Based Neural Architecture Search [3.287802528135173]
  We study predictor-based NAS algorithms for neural architecture search. We show that the sample efficiency of predictor-based algorithms decreases dramatically if the proxy is only computed for a subset of the search space. This is an important step to make predictor-based NAS algorithms useful, in practice.
  arXiv  Detail & Related papers  (2020-11-24T11:36:36Z)
• ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding [86.40042104698792]
  We formulate neural architecture search as a sparse coding problem. In experiments, our two-stage method on CIFAR-10 requires only 0.05 GPU-day for search. Our one-stage method produces state-of-the-art performances on both CIFAR-10 and ImageNet at the cost of only evaluation time.
  arXiv  Detail & Related papers  (2020-10-13T04:34:24Z)
• NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size [31.903475598150152]
  We propose NATS-Bench, a unified benchmark on searching for both architecture topology and size. NATS-Bench includes the search space of 15,625 neural cell candidates for architecture topology and 32,768 for architecture size on three datasets.
  arXiv  Detail & Related papers  (2020-08-28T21:34:56Z)
• DC-NAS: Divide-and-Conquer Neural Architecture Search [108.57785531758076]
  We present a divide-and-conquer (DC) approach to effectively and efficiently search deep neural architectures. We achieve a $75.1%$ top-1 accuracy on the ImageNet dataset, which is higher than that of state-of-the-art methods using the same search space.
  arXiv  Detail & Related papers  (2020-05-29T09:02:16Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)

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.