Can you shrink the network and still maintain acceptable accuracy? Bennett et al. This means that findings are reported to be significant when in fact they have occurred by chance. The checklist can help verify several components of a study we are reproducing, placing particular emphasis on empirical methods. Without methods reproducibility, scientists risk claiming gains from changing one parameter while the real source of improvement may be some hidden source of randomness. We can follow a checklist developed by Joelle Pineau and her group which we will talk more about in a later section. Ioannidis [1] makes some bold statements when he proclaims that “Most research findings are false, for most research designs and for most fields” and “It can be proven that most claimed research findings are false”. “Independent discussion sections for improving inferential reproducibility in published research.” British journal of anaesthesia 122, no. These conclusions are usually listed in the abstract, introduction and discussion sections. A reproducibility program was introduced, designed to improve the standards across the community and evaluate ML research. What is reproducibility in ML. Welcome to the ML Reproducibility Challenge 2020! A t-contrast was used to test for regions with significant signal change during the photo condition compared to rest but no corrections were performed. Joelle Pineau, a professor at McGill University, and her group have studied reproducibility in reinforcement learning. This has led the top ML conferences to strongly encourage the submission of code for accepted papers in the last year. Finally, it is imperative for any researcher who is testing for multiple hypotheses to perform suitable corrections. . We can see from this equation that PPV decreases as the number of independent studies grows unless $$1-\beta < \alpha$$. For instance. According to Goodman et al. “Enriching word vectors with subword information.” Transactions of the Association for Computational Linguistics 5 (2017): 135-146. To simplify the calculations, we assume that all the studies addressing the same sets of research questions are independent and that there is no bias. Then, voxelwise statistics were computed using a general linear model (GLM). He/she should also accurately report all versions of the experimental protocol to assure the reader that selective outcome reporting has not occurred. As shown in Figure 4, again, differences in performance were observed. For example, a researcher with a bias to show that his/her hypothesis is correct may try to reject the null hypothesis by manipulating the data and justify it in the name of outlier removal. Thus, results must be evaluated after aggregating them across relevant studies and not independently. We need to provide a clear description about the data collection process, and how samples were allocated for training, testing, and validation. Biased inferences have the potential to create inaccurate scientific narratives that can persist in research. . Figure 1: Summary diagram highlighting the different steps in a data analysis project and the questions one should ask at each step in order to overcome the barriers to various types of reproducibility. This paper was later retracted on the grounds that an independent replication study by Engstrom et al found the methods less effective than claimed [7]. [15] on the association between various bias patterns and overestimation of effect sizes. Experiment reproducibility. We can see from this equation that PPV decreases as the number of independent studies grows unless $$1-\beta < \alpha$$. In addition, there are no universal best practices on how to archive a training process so it can be successfully rerun in the future. Table 4: Results reported by Fanelli et al. Welcome to the Community Portal for the UCI ML Repository. This is the case since PPV depends on the pre-study odds $$R$$ of a true finding. A subreddit dedicated to learning machine learning. Table 5: Results reported by Fanelli et al. Before we go further, we need to understand what reproducibility in the context of machine learning really means. What does this mean? In the same vein, when Bayer attempted to replicate some drug target studies, 65% of the studies could not be replicated [18]. Without it, data scientists risk claiming gains from changing one parameter without realizing that hidden sources of randomness are the real source of improvement. In Machine Learning, programs can have the same meaning and even speak the same language but output different results because context matters and implementations are full of (intended) randomness. When the authors performed the correction by either controlling the false discovery rate (FDR) or by controlling the overall familywise error rate (FWER) through the use of Gaussian random field theory, all of the previously significant regions were no longer significant. Their results are highlighted in Table 5. This is especially difficult in machine learning and deep learning since many times we do not know why, how or to what extent our model works. User account menu • Guidelines for reproducibility in ML R&D (ask away, I'm here to clarify) Discussion. With reproducible research in ML being as trivial as releasing your code on github, and remembering to use set.seed(1234) (or equivalent) - there’s really no excuse not to get perfect reproducibility in ML. Bias may lead to overinterpretation or misinterpretation of the results to suit the researcher’s hypothesis. fMRI measures the activity of a brain region by detecting the amount of blood flowing through it – a region which was active during a cognitive task is likely to have increased blood flow. This may help in increasing the PPV and in effectively choosing research areas. In today’s age, the more hypotheses we examine, the more likely that we find statistically significant results that are mere coincidences. which is the probability of producing a research finding even though it is not statistically significant purely because of bias. Sometimes, even the same labs can’t reproduce their previous findings. Figure 5 shows this trend. Junior researchers were also more likely to publish false findings according to their results. This renewed interest around replicability of results was kickstarted at last year’s NeurIPS conference, the premier international conference for research in ML. Fanelli et al. See the About page for more details. The interpretation of the results obtained using clustering techniques can also be highly subjective and different people can perceive them in different ways. We have briefly looked at bias as a barrier to inferential reproducibility in a previous section. Ioannidis relies on post predictive value (PPV) which defines the probability of a finding being true, after a finding has been claimed based on achieving formal statistical significance. Eliminating discussion sections altogether could be another way of avoiding biased conclusions but it is quite extreme and might only work in journals which are aimed at very specific audiences who will go through the methods and results section anyway. “A manifesto for reproducible science.” Nature human behaviour 1.1 (2017): 0021. https://health.ucdavis.edu/welcome/features/20071114_cardio-tobacco/, https://courses.lumenlearning.com/waymaker-psychology/chapter/reading-parts-of-the-brain/, Using Deep Learning to Help Us Understand Language Processing in the Brain, FACT Diagnostic: How to Better Understand Trade-offs Involving Group Fairness. Decline effect: Early studies in a scientific field tend to overestimate effect sizes when compared to later studies because publication bias (bias to only publish significant/positive results) has reduced over time and later studies also tend to use more accurate methods. Reproducibility in ML Reproducibility has been an ongoing topic of discussion amongst the machine learning community. While evaluating the statistical significance of a given set of results, we must “correct” for the fact that multiple teams are working on the same problem. Following are the main points from her checklist: The checklist can help verify several components of a study we are reproducing, placing particular emphasis on empirical methods. For reproducibility, we have to record the full story and keep track of all changes. https://media.neurips.cc/Conferences/NIPS2018/Slides/jpineau-NeurIPS-dec18-fb.pdf. The four most commonly used baselines for policy gradient algorithms are TRPO, PPO, DDPG, and ACKTR. A work is said to be reproducible when a reader follows the procedure listed in the paper and ends up getting the same results as shown in the original work. Bias is modeled using a parameter $$u$$ which is the probability of producing a research finding even though it is not statistically significant purely because of bias. [15] on the association between various factors and overestimation of effect sizes. Reproducibility in Science. Results reproducibility is a stronger check and concerns itself with the ability to reproduce a set of results on another dataset or using a slightly different method on the original study’s dataset. Pete Warden [8] describes the typical life cycle of a machine learning model in image classification as follows: The researcher uses a local copy of one of the ImageNet datasets. Pressures to publish: Direct or indirect pressures to publish can force scientists to exaggerate the magnitude and importance of their results so as to increase the visibility of their papers and/or their number of publications. Sample size becomes important in case of an independent replication study. Suppl 1 (2009): S125. The main goal of this challenge was to encourage people to reproduce results from ICLR 2018 submissions, where the papers are readily available on OpenReview. This means that findings are reported to be significant when in fact they have occurred by chance. Table 1 shows the notation used by Ioannidis. However, when another researcher, Stephane Doyen, tried to replicate the result he found no impact on the volunteers’ behaviors. Ioannidis, John PA. “Why most published research findings are false.” PLoS medicine 2.8 (2005): e124. OpenML is an open online platform where one cannot only share datasets, but also entire machine experiments. You start by training a model from the existing architecture so youâll have a baseline to compare against. The situation is more severe in some fields such as chemistry and biology since it can take years to fully replicate a study, making it incredibly hard to check for reproducibility. We provide guidelines for improving the reproducibility of deep-learning models, together with the Python package dtoolAI, a proof-of-concept implementation of these guidelines. Only having fair comparisons with the same amount of data and compute is not sufficient. At AAAI, Odd Erik Gundersen, a computer scientist at the Norwegian University of Science and Technology, reported the results of a survey of 400 algorithms presented in papers at two top AI conferences in the past few years. Given its complexity, fully enabling reproducibility will require effort across the stack - from infrastructure-level developers all the way up to ML framework authors. This also means that the shared experiments can be used in many innovative ways. Gender of a scientist: Statistics from the US Office of Research Integrity supports the hypothesis that men are more likely to engage in QRPs than women although there might be many alternative explanations for these statistics. US effect: Certain sociological factors might cause authors from the US to overestimate effect sizes. To mitigate this issue, after the initial Reproducibility in Machine Learning workshop at ICML 2017, Dr. Joelle Pineau and her colleagues started the first version of the Reproducibility challenge at ICLR 2018. From the table, we can easily compute the PPV as a ratio of highlighted values. By contrast, the values of other parameters (typically node weights) are derived via training. This vivid experiment illustrates how it is possible to obtain significant yet false findings when we test many hypotheses but do not correct for it. Rerunning our experiment in Determined, hereâs what we see: As we hoped, the resulting validation error is now almost identical to our baseline. Again, this type of scrutiny helps debunk false claims. First log in to your OpenReview account, and then navigate to your paper (s) in ML Reproducibility Challenge homepage. This vivid experiment illustrates how it is possible to obtain significant yet false findings when we test many hypotheses but do not correct for it. identify the following prevalent bias patterns: Fanelli et al. Thus, such studies are usually found in lesser known venues such as conference proceedings (not applicable to computer science but is quite applicable to other scientific fields), books, personal communications, and other forms of “gray” literature. Reproducing a study is a common practice for most researchers as this can help confirm research findings, get inspiration from others’ work, and avoid reinventing the wheel. Now let us talk about whether there’s a mathematical justification behind why reproducibility is hard. J Natl Cancer Inst 96: 434–442. She publishes her results, with code and weights of the model. They also experimented with the hyperparameters of the algorithms, and found that the algorithms were highly sensitive to their hyperparameters, and yielded very different results with varied hyperparameters. He is also an Apple alumnus and blogs at petewarden.com.. Press J to jump to the feed. Bennett, Craig M., Michael B. Miller, and G. L. Wolford. Gray literature bias: Reputed venues often do not accept studies which show small and/or statistically insignificant effects. Munafò, Marcus R., et al. Pineau and her group developed a reproducibility checklist [9] enlisting points that help towards methods as well as results reproducibility. Mutual control: Close collaborations can keep questionable research practices (QRPs) in check because many sets of eyes look at the results of such collaboration in contrast to long-distance collaborations where it is harder for all results to be thoroughly analyzed for correctness. By building first class support for it into our tools, we hope to further increase the visibility of this issue. Inferential reproducibility is quite important to both scientific audiences and the general public. Reproducibility in ML. [15] in their work on bias and its root causes. We first introduce the reproducibility crisis in science and motivate the need for asking these questions. 2mm liners hold approx. Deep learning has brought impressive advances in our ability to extract information from data. From these calculations, Ioannidis deduces that the smaller the sample sizes used to conduct experiments in a scientific field, the less likely the research findings are to be true. Fanelli et al. In general, reproducibility reduces the risk of errors, thereby increasing the reliability of an experiment. As shown in Figure 3, they observed different performances for each of these algorithms across different environments. “Meta-assessment of bias in science.” Proceedings of the National Academy of Sciences 114, no. If a fellow researcher in the community is not able to reproduce the results you are claiming then there might be something wrong in your experiment setup. Well, we can start by capturing all the metadata associated with an experiment, and systematically addressing the common causes listed above. This insightful analysis sheds some light on what bias patterns we should keep in mind while evaluating a study – mostly the small study effect, the citation bias and the gray literature bias. Without the ability to compare the results of a training session from Model A to that of Model B, there is no way to know what changes led to improvement or degradation of results Research iterations require, by definition, changes between rounds. According to Ioannidis [1], bias is the amalgamation of many design, data, analysis, and presentation factors that may lead to the production of research findings when they should not have been produced. We need to realise that often times, which algorithm performs the best depends on the amount of data and compute budget. [13] demonstrated this using a functional magnetic resonance imaging (fMRI) experiment. We need to include a clear definition of statistics used to report the results, description of results including central tendency, variance, error bars as well as number of evaluation runs. Figure 1 shows the steps involved in a data analysis project and the questions a researcher should ask to help ensure various kinds of reproducibility. Based on these variables, Table 2 shows the probabilities of different combinations of research conclusions versus ground truth. Statistical power is given by $$1 – \beta$$, and it is the probability of finding a true relationship. Hence, it is imperative for these sections to be dispassionate, pertinent and demonstrably reproducible. Statistical power is given by $$1 – \beta$$, and it is the probability of finding a true relationship. [instagram-feed num=6 cols=6 imagepadding=0 disablemobile=true showbutton=false showheader=false followtext=”Follow @Mint_Theme”], Legal Info | www.cmu.edu Then, voxelwise statistics were computed using a general linear model (GLM). Table 3 shows the effects of bias on the various probabilities which were introduced in Table 2. Avidan, Michael S., John PA Ioannidis, and George A. Mashour. [13] demonstrated this using a functional magnetic resonance imaging (fMRI) experiment. The researcher uses a local copy of one of the ImageNet datasets. An example of this situation is when drugs company Amgen tried to replicate some of the landmark publications in the field of cancer drug development for a report published in. Reproducibility of experiments is one of the main principles of the scientific method. A very common problem is that the source code used in a study is not open-sourced. 4 (2019): 413-420. Kannan, Harini, Alexey Kurakin, and Ian Goodfellow. The analysis also showed that highly cited articles and articles in reputed venues could also present overestimated effects. Wacholder S, Chanock S, Garcia-Closas M, Elghormli L, Rothman N (2004) Assessing the probability that a positive report is false: An approach for molecular epidemiology studies. Finally, they found partial evidence to support the claims that males and individuals with questionable integrity are more likely to overestimate effects. Conclusions which are based on these false findings cannot be trusted and thus act as major barriers to inferential reproducibility. This website is the hub for the development plans and updates and community event highlights around the UCI’s machine learning repository. Reproducing results across machine learning experiments is painstaking work, and in some cases, even impossible. [10], there are three types of reproducibility: methods, results, and inferential reproducibility. Tyler Vigen [5] shows examples of many such correlations discovered in evidently unrelated sets of data. Ioannidis relies on post predictive value (PPV) which defines the probability of a finding being true, after a finding has been claimed based on achieving formal statistical significance. Only a third shared the data they tested their algorithms on, and just half shared pseudocode [17]. Noisy hidden layers: Many architectures for neural networks include layers with inherent randomness. Barriers to methods reproducibility. It is common in academia for multiple teams to focus on similar or the same problems. Power is also proportional to the effect size, and thus, the larger the effect size, the higher the power, and thus higher PPV. Reproducibility has been an ongoing topic of discussion amongst the machine learning communities. Experiments that report results that do not clearly specify any of these elements are very likely not easily reprod… Modeling the Framework for False Positive Findings, Here, the type I error indicates a false positive. Results reproducibility is defined as the ability to produce corroborating results in a new (independent) study having followed the same experimental procedures [10]. He is formerly the CTO of Jetpac, which was acquired by Google. 11 (2014): e112575. In fact, ML researchers have found it difficult to reproduce many key results and it is leading to a new conscientiousness about research methods and publication protocols. Further, development in ML is very sensitive to changes and even small differences can have high impacts on the result. Figure 7 shows the significant regions. She tries many different ideas, fixing bugs, tweaking the code, and changing hyperparameters on her local machine. Thus, $$PPV = \frac{(1 − \beta)R + u \beta R}{R + \alpha − \beta R + u − u\alpha + u\beta R}$$ I, t is apparent that when the amount of bias quantified by $$u$$, increases, the PPV decreases, unless $$1-\beta \le \alpha$$. The engineer who developed the original model is on leave for a few months, but not to worry, youâve got the model source code and a pointer to the dataset. Now, we shall analyze its effect on the PPV, the implications and also understand various bias patterns and their root causes. Over the past decade, this problem has plagued many scientific fields. Furthermore, using our explicit âreproducibilityâ flag, users can control the randomness affecting batch creation, weights, and noise layers across experiments. This is because most readers of a paper form an opinion of it based on the conclusions of the authors. August 5, 2020. Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. Reproducibility is a minimum requirement for a finding to be believable and informative, yet more than 70% of researchers have reported failure to reproduce another scientist’s experiments [16]. Track everything you need for every experiment run. Finally, we need to have a specification of the hyperparameters. At the minimum, to reproduce results, the code, data, and overall platform need to be recorded accurately. This means that findings that are actually significant fail to be reported. It could not replicate 47 out of 53 studies [18]. It is quite clear from the results that the aforementioned subject was not human – it was a dead Atlantic Salmon! Collecting evidence which provides more statistical power (ex. This quantitatively shows that increased bias leads to an increase in the number of false research findings. What we want is for a researcher to be able to easily play around with new ideas, without having to pay a large “process tax” in terms of getting an existing code (e.g. Each of these types of reproducibility is extremely important in their own regard and we have given some examples of adverse consequences when they are not enforced. Given implications of increased bias for research reproducibility, let us look at some common bias patterns as identified by Fanelli et al. Looking at the plot of your modelâs validation error alongside that of your teammate leaves you scratching your head. As a refresher, Figure 8 shows the different steps in a data analysis project, and at each step, the reproducibility pitfalls that can creep in, if appropriate measures are not exercised. Moreover, there are also multiple causes of non-determinism: In general, the process of documenting everything is extremely manual and time-consuming. It’s just that in this historical period is characterised by a particular hype (maybe excessive) about ML (and AI). Ioannidis also makes the observation that the smaller the effect sizes in a scientific field, the less likely the research findings are to be true. Finally, as a community, we should strive to enforce reproducibility by strengthening the peer review process to include code review, writing of neutral discussion sections and possibly even lifting paper length constraints. The analysis partially supported the claim that industry bias and the US effect lead to overestimation while it did not support the claim that early extreme studies overestimate effect sizes. This post candidly discusses some of the real world reproducibility challenges that are happening within ML model collaboration, specifically potential … Repeatability is the key to good science. It is quite easy to forget to adjust the significance of the reported results to account for the multitude of hypotheses that have been tested. https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/, https://www.cs.mcgill.ca/~jpineau/ReproducibilityChecklist.pdf. Now that we understand the importance of and the barriers to inferential reproducibility, let us look at some suggestions to help us overcome these barriers. In fact, this checklist was adopted as a mandatory requirement for papers submitted to, Bias may lead to overinterpretation or misinterpretation of t. he results to suit the researcher’s hypothesis. We need to have robust conclusions. Let’s delve into some of the math behind these claims, and then we will show a few real-life cases which support the corollaries he derives from the analysis. However, they found that 5 was in fact towards the higher end of values that were usually used for n. What conclusions can be drawn from the study? Changes in machine learning frameworks: This may lead to different behaviours across versions and more discrepancies across frameworks. Table 3 shows the effects of bias on the various probabilities which were introduced in Table 2. In the forum associated with your paper, you can find a dropdown to "Notification Subscription:" and you may choose the subscription types "Subscribe" and "Unsubscribe" to follow and unfollow activities surrounding your paper. We want that a researcher be able to step in for a colleague, train all the models exactly, and get the exact results. The inference time on an existing ML model is too slow, so the team wants you to analyze the performance tradeoffs of a few different architectures. In this blog post, we will introduce the different types of reproducibility and delve into the reasons for the formulation of these questions at each step. Based on a combination of masochism and stubbornness, over the past eight years I have attempted to implement various ML algorithms from scratch. This shift not only multiplies the sources of non-determinism but also increases the need for both fault tolerance and iterative model development. Half of the experimenters were told to expect slow movements from the volunteers, and the other half, to expect faster movements. We will be discussing different bias patterns and their underlying causes in a later section. Citation bias: Studies which show large effect sizes are usually cited more often than ones that show smaller effects. causing ghost peaks and reproducibility problems in chromatography. Both of these fields have long histories and have multiple companies and/or research groups working on similar problems, possibly leading to the aforementioned reproducibility issues. Reproducibility ensures correctness. In order to encourage more open, transparent and accessible science, there have been many efforts around reproducibility. Finally, instead of chasing statistical significance, we should improve our understanding of the range of $$R$$ values, the pre-study odds, in various research fields [19]. While the program is in progress, she might change the code locally – since training can take a lot of time to complete. collected more than 3000 meta-analyses (analyses of previously published studies) from various scientific fields and tried to determine which bias patterns were prevalent in the literature using a regression-based analysis – they tested if each pattern is associated with a study’s likelihood to overestimate effect sizes. A t-contrast was used to test for regions with significant signal change during the photo condition compared to rest but no corrections were performed. Problem has plagued many scientific fields context, it relates to getting same output on algorithm. Lead to overinterpretation or misinterpretation of the authors placing particular emphasis on methods... Tensorflow Mobile Embedded team at Google doing Deep learning can easily compute the PPV, the type I indicates. 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