Exemplar Pro Font Free Download
Mrx Wylie
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The term "multiple exemplar training" - sometimes referred to as "multiple exemplar instruction" - basically means using multiple examples when training (or teaching) a child. The use of multiple exemplars is an important element of any educational programme and is not limited to an Applied Behaviour Analysis (ABA) intervention.
The next time you walk through a town or city, have a look at all of the different types of fonts used in signs and advertising. This might help you realise how important it is to use multiple exemplars in an educational programme.
There are two types of generalisation, the first is stimulus generalisation and the second is response generalisation (Cooper, Heron, & Heward, 2007). Generalisation occurs when you do something that you were not specifically trained to do or identify something that you were not trained to identify.
To describe stimulus generalisation, consider what it might be like when you go into a shop to buy a new pair of scissors. This shop is unlikely to have a specific pair of scissors that you have ever seen before. Yet you would be very capable of identifying a pair and buying them.
Although you would never have seen the exact pair of scissors you will buy before, you would still know they were scissors because you know that in general scissors have a handle for your fingers/thumb and have two blades that swivel around a fixed joint.
A pair of scissors is a type of stimulus and because you have seen numerous other examples of scissors in the past then you know what defines them and so your ability to identify scissors has generalised.
Example 4: Emily has learned to drive her Ferrari and her Lamborghini. She decides she wants a Porsche now and goes to the dealer. She buys the car, hops in and drives it away. Although Emily had never driven the Porsche before she knew how to drive in general and so was able to generalise her knowledge of driving her Ferrari and Lamborghini to driving a different car.
One of the challenges in providing students with the most significant disabilities with access to the general curriculum is finding materials that link directly to the grade level content, but are written at a level that is accessible. As part of the DLM project we have been building a library of companion texts that go with the exemplar texts called out in the Appendix of the College and Career Readiness Standards. These books are accessible, open-source texts that you and your students can read online, on a reader that uses epub files, or offline as Powerpoint files or printed versions of the books.
The main novelty of our approach lies in the fact that the exemplars and counter-exemplars produced by xspells are meaningful texts, albeit synthetically generated. We map the input text x from a (sparse) high-dimensional vector space into a low-dimensional latent space vector z by means of Variational Autoencoders Kingma and Welling (2014), which are effective in encoding and decoding diverse and well-formed short texts (Bowman et al. 2016). Then we study the behavior of the black box b in the neighborhood of z, or, more precisely, the behavior of b on texts decoded back from the latent space. Finally, we exploit a decision tree built from latent space neighborhood instances to drive the selection of exemplars and counter-exemplars. Experiments on three standard datasets and two black box classifiers show that xspells overtakes the baseline method lime Ribeiro et al. (2016) by providing understandable, faithful, useful, and stable explanations.
This paper extends the conference version Lampridis et al. (2020) in several aspects. First, we formulate the problem of diverse counter-exemplar selection and provide a solution based on a greedy algorithm. Second, in addition to training a VAE on a subset of available data, we consider using a pre-trained VAE from the state of the art. Third, a deeper experimental qualitative/quantitative analysis is conducted. The rest of the paper is organized as follows. Section 2 discusses related work. Section 3 formalizes the problem and recalls key notions for the proposed method, which is described in Sect. 4. Section 5 presents the experimental results. Finally, Sect. 6 summarizes our contribution, its limitations, and future work.
Research on interpretability and explainability in AI has bloomed over the last few years (Guidotti et al. 2019b; Miller 2019), with many implementations of proposed methods (Bodria et al. 2021; Linardatos et al. 2021). Intrinsically explainable AI models can be directly interpretable by humans, or the explanation of their decisions arise as part of their prediction process (self-explainability). Examples in the area of short text classification include linear classifiers exploiting word taxonomies (Skrlj et al. 2021) or lexicons (Clos and Wiratunga 2017). The best performing text classifiers, however, rely on black box models, which are inaccessible, inscrutable, or simply too complex for humans to understand. Hence, they require post-hoc explanations of their decisions. Explanation methods can be categorized as: (i) Model-specific or model-agnostic, depending on whether the approach requires access to the internals of the black box; (ii) Local or global, depending on whether the approach explains the prediction for a specific instance or the overall logic of the black box.
xspells falls into the category of local, model-agnostic methods. Well known tools in this category that are able to work also on textual data include lime, anchor and shap. lime Ribeiro et al. (2016) randomly generates synthetic instances in the neighborhood of the instance to explain. An interpretable linear model is trained from such instances. Feature weights of the linear model are used for explaining the feature importance over the instance to explain. In the case of texts, a feature is associated to each of the top frequent words in a dataset. lime has two main weaknesses. First, the number of top features/words to be considered is assumed to be provided as an input by the user. Second, the neighborhood texts are generated by randomly removing words, possibly generating meaningless texts. anchor Ribeiro et al. (2018) follows the main ideas of lime but it returns decision rules (called anchors) as explanations. In the case of texts, such rules state which words, once fixed, do not alter the decision of the black box when randomly replacing all other words by similar words (in an embedding space) with the same part-of-speech (POS) tag. anchor adopts a bandit algorithm that constructs anchors with predefined minimum precision. Its weaknesses include the need for user-defined precision threshold parameters, and, as for lime, the generation of possibly meaningless instances. shap Lundberg and Lee (2017) relates game theory with local explanations and overcomes some of the limitations of lime and anchor. Also shap audits the black box with possibly meaningless synthetic sentences. The method xspells proposed in this paper recovers from this drawback by generating the sentences for the neighborhood in a latent space by taking advantage of variational autoencoders.
Regarding counter-factual approaches, while there is a growing literature for tabular data and images (Artelt and Hammer 2019; Verma et al. 2020), to the best of our knowledge our proposal is an original contribution in the context of short text classification. A form of contrastive explanations has been proposed in the local model-specific approach of Croce et al. (2019) for a self-explaining question classification system based on LRP. Here, the top texts from the training set which contribute the most (negatively) to the decision are returned as counter-exemplars.
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