Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing

We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business applications will have some form of ML. However, testing such applications is extremely challenging and would be very expensive if we follow today's methodologies. In this work, we present an articulation of the challenges in testing ML based applications. We then present our solution approach, based on the concept of Metamorphic Testing, which aims to identify implementation bugs in ML based image classifiers. We have developed metamorphic relations for an application based on Support Vector Machine and a Deep Learning based application. Empirical validation showed that our approach was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2018

Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems - IEEE CBMS 201

Dimensionality reduction methods for machine translation quality estimation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-013-9139-3[EN] Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets.This work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no. 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).González Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Dimensionality reduction methods for machine translation quality estimation. Machine Translation. 27(3-4):281-301. https://doi.org/10.1007/s10590-013-9139-3S281301273-4Amaldi E, Kann V (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci 209(1–2):237–260Anderson TW (1958) An introduction to multivariate statistical analysis. 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In: Proceedings of the seventh workshop on statistical machine translation, pp 10–51Chong I, Jun C (2005) Performance of some variable selection methods when multicollinearity is present. Chemom Intell Lab Syst 78(1–2):103–112Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297Gamon M, Aue A, Smets M (2005) Sentence-Level MT evaluation without reference translations: beyond language modeling. In: Proceedings of the conference of the European Association for Machine TranslationGandrabur S, Foster G (2003) Confidence estimation for text prediction. In: Proceedings of the conference on computational natural language learning, pp 315–321Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185(1):1–17González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) Balancing user effort and translation error in interactive machine translation via confidence measures. In: Proceedinss of the meeting of the association for computational linguistics, pp 173–177González-Rubio J, Sanchís A, Casacuberta F (2012) Prhlt submission to the wmt12 quality estimation task. In: Proceedings of the seventh workshop on statistical machine translation, pp 104–108Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. Machine Learning Research 3:1157–1182Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18Hotelling H (1931) The generalization of Student’s ratio. Ann Math Stat 2(3):360–378Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the association for computational linguistics, demonstration sessionKohavi R, John GH (1997) Wrappers for feature subset selection. 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In: Proceedings of the fifteenth symposium on the interface, computer science and statistics, pp 173–179Soricut R, Echihabi A (2010) TrustRank: inducing trust in automatic translations via ranking. In: Proceedings of the meeting of the association for computational linguistics, pp 612–621Soricut R, Bach N, Wang Z (2012) The SDL language weaver systems in the WMT12 quality estimation shared task. In: Proceedings of the seventh workshop on statistical machine translation. Montreal, Canada, pp 145–151Specia L, Saunders C, Wang Z, Shawe-Taylor J, Turchi M (2009a) Improving the confidence of machine translation quality estimates. In: Proceedings of the machine translation summit XIISpecia L, Turchi M, Cancedda N, Dymetman M, Cristianini N (2009b) Estimating the sentence-level quality of machine translation systems. In: Proceedings of the meeting of the European Association for Machine Translation, pp 28–35Tibshirani R (1996) Regression shrinkage and selection via the lasso. 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Multiple segmentations of Thai sentences for neural machine translation

Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more complexity when building sequence to sequence models. In this work, we explore how to augment a set of English–Thai parallel data by replicating sentence-pairs with different word segmentation methods on Thai, as training data for NMT model training. Using different merge operations of Byte Pair Encoding, different segmentations of Thai sentences can be obtained. The experiments show that combining these datasets, performance is improved for NMT models trained with a dataset that has been split using a supervised splitting tool

Neuron-level fuzzy memoization in RNNs

The final publication is available at ACM via http://dx.doi.org/10.1145/3352460.3358309Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems. For each application run, each recurrent layer is executed many times for processing a potentially large sequence of inputs (words, images, audio frames, etc.). In this paper, we make the observation that the output of a neuron exhibits small changes in consecutive invocations. We exploit this property to build a neuron-level fuzzy memoization scheme, which dynamically caches the output of each neuron and reuses it whenever it is predicted that the current output will be similar to a previously computed result, avoiding in this way the output computations. The main challenge in this scheme is determining whether the new neuron's output for the current input in the sequence will be similar to a recently computed result. To this end, we extend the recurrent layer with a much simpler Bitwise Neural Network (BNN), and show that the BNN and RNN outputs are highly correlated: if two BNN outputs are very similar, the corresponding outputs in the original RNN layer are likely to exhibit negligible changes. The BNN provides a low-cost and effective mechanism for deciding when fuzzy memoization can be applied with a small impact on accuracy. We evaluate our memoization scheme on top of a state-of-the-art accelerator for RNNs, for a variety of different neural networks from multiple application domains. We show that our technique avoids more than 24.2% of computations, resulting in 18.5% energy savings and 1.35x speedup on average.Peer ReviewedPostprint (author's final draft