Augment &amp; Valuate : A Data Enhancement Pipeline for Data-Centric AI

Lee, Youngjune; Kwon, Oh Joon; Lee, Haeju; Kim, Joon‐Young; Lee, Kangwook; Kim, Kee-Eung

doi:10.48550/arxiv.2112.03837

Cited by 3 publications

(5 citation statements)

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“…Alternatively, data-centric methods-which leave the system's architecture unchanged-endeavor to improve system performance by using enhanced data [27][28][29] obtained through, e.g. data set augmentation [30][31][32], data set distillation [33,34], label analysis and accuracy improvements [35][36][37][38], data set validation [39,40], domain randomization [41], and combinations of these [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…Data-centric ML techniques have found wide applicability in a variety of domains including legal [43], natural language processing [37], image classification [44], and medical prognosis [39]. In the context of QIS, data-centric approaches, namely engineered data sets, have been used to demonstrate prediction advantage in quantum machine learning [47] and improve the accuracy of state reconstruction systems [48][49][50].…”

Section: Introductionmentioning

confidence: 99%

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Data-centric machine learning in quantum information science

Lohani

Lukens

Glasser

et al. 2022

Mach. Learn.: Sci. Technol.

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We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ``toy model'' demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-centric machine learning in quantum information science

Lohani

Lukens

Glasser

et al. 2022

Mach. Learn.: Sci. Technol.

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“…Examples include increasing the number of hidden layers in a deep neural network, tailoring the structure of a model, modifying the loss function [26] or tweaking the reward function in reinforcement learning. Alternatively, data-centric [27][28][29][30][31][32][33][34][35][36] methods-leaving the system's architecture unchanged-endeavor to improve system performance by using enhanced data sets, e.g., removing spurious correlations, increasing the accuracy of labels, increasing the variety of sampled situations covered by the data, or distilling the data sets to improve efficiency [37].…”

Section: Introductionmentioning

confidence: 99%

“…Data-centric ML techniques have found wide applicability in a variety of domains including legal [32], natural language processing [40], image classification [35], and medical prognosis [36]. In the context of QIS, a datacentric approach to improving state reconstruction accuracy was implemented where expected statistical and experimental noise were included into training sets of a convolutional neural network (CNN), resulting in overall performance improvements [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Data-Centric Machine Learning in Quantum Information Science

Lohani¹,

Lukens²,

Glasser³

et al. 2022

Preprint

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has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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“…In DC-AI, more emphasis is on data; the model can simply be used/imported, and therefore, transfer learning can play a vital role in this new paradigm. • Data-enhanced pipelines (or instruments): To solve noisy data and scarcity issues, many sophisticated pipelines have been developed recently to enhance the performance of machine learning techniques [136]. These pipelines can significantly enhance the accuracy of ML methods by utilizing a small portion of data.…”

mentioning

confidence: 99%

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI

Cited by 3 publications

References 0 publications

Data-centric machine learning in quantum information science

Data-centric machine learning in quantum information science

Data-Centric Machine Learning in Quantum Information Science

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

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