Titlebook: Artificial Intelligence and Machine Learning; 32nd Benelux Confere Mitra Baratchi,Lu Cao,Frank W. Takes Conference proceedings 2021 Springe

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https://doi.org/10.1007/978-3-031-62505-3 We explore whether recent state-of-the-art models are capable of recognizing two paraphrased questions using unsupervised learning. Firstly, we test QA models’ performance on an existing paraphrased dataset (Dev-Para). Secondly, we create a new paraphrased evaluation set (Para-SQuAD) containing mul

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Teryn Bruni,Elizabeth Koval,Luke Turniermine its saliency. Our method can perform pruning before the network has converged, requires little tuning effort due to having good default values for its hyperparameters, and can directly target the level of sparsity desired by the user. Our experiments, performed on a variety of object classifica

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Caroline Lucy,Julie Wojtaszek,Leah LaLondevaluations. The surrogate, which is cheaper to evaluate, is optimized instead to find an approximate solution to the original problem. In the case of discrete problems, recent research has revolved around discrete surrogate models that are specifically constructed to deal with these problems. A main

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Teryn Bruni,Elizabeth Koval,Luke Turnier. However, if the predictions of an accurate algorithm are aggregated, for example by counting the predictions of a single class label, the result is often still statistically biased. Implementing machine learning algorithms in the context of official statistics is therefore impeded. The statistical

irritation

Paggie Kim,Jennifer Burns-Benggon,Haley Reisties for making use of efficient neuromorphic hardware. Two types of implementation are explored that offer a complexity trade-off between computational space and time, with both designs having linear energy complexity. The designs were implemented in a simulator to successfully solve the one-max op