Titlebook: Machine Learning, Optimization, and Data Science; 6th International Co Giuseppe Nicosia,Varun Ojha,Renato Umeton Conference proceedings 202

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configuration

Automatic Curriculum Recommendation for Employees,or peer feedback. The system integrates content-based and interested-based recommendations across multiple data-streams and interaction modalities to arrive at superior recommendations to those based on just content or interests. The training assets span a wide variety of content formats such as blo

convert

Target-Aware Prediction of Tool Usage in Sequential Repair Tasks, of tool usage would be helpful for various assistance scenarios, e.g. allowing a contextualized assistant to predict the next required tool in an unseen task. In this work, we examine the potential of this idea. We employ two prominent classes of sequence learning methods for modeling the tool usag

猛烈责骂

Safer Reinforcement Learning for Agents in Industrial Grid-Warehousing,larly challenging. Here, current state-of-the-art reinforcement learning algorithms struggle to learn optimal control policies safely. Loss of control follows, which could result in equipment breakages and even personal injuries..On the other hand, a model-based reinforcement learning algorithm aims

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Coking Coal Railway Transportation Forecasting Using Ensembles of ElasticNet, LightGBM, and Faceboon two directions: export and domestic transportation. We built ensembles of ElasticNet, LightGBM, and Facebook Prophet models. The coal export transportation volumes are best predicted by an ensemble of ElasticNet and LightGBM models, giving the mean absolute percentage error at 10%. The best model

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FICE

High-Dimensional Constrained Discrete Multi-objective Optimization Using Surrogates,nd many black-box constraints. The algorithm builds and maintains multiple surrogates to approximate each of the black-box objective and constraint functions. The surrogates are used to identify promising sample points for the function evaluations from a large number of trial solutions in the neighb

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Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations, original input. To find adversarial examples, some attack strategies rely on linear approximations of different properties of the models. This opens a number of questions related to the accuracy of such approximations. In this paper we focus on DeepFool, a state-of-the-art attack algorithm, which i

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Lottery Ticket Hypothesis: Placing the k-orrect Bets,., the authors showed that pruning is a way of training, which we extend to show that pruning a well-trained network at initialization does not exhibit significant gains in accuracy. . motivates us to explore pruning after . epoch. We show that there exists a minimum value of . above which there is

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