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In Model-Driven Engineering (MDE), models are used to build and analyze complex systems. In the last decades, different modelling formalisms… (voir plus) have been proposed for supporting software development. However, their adoption and practice strongly rely on mastering essential modelling skills to develop a complete and coherent model-based system. Moreover, it is often difficult for novice modellers to get direct and timely feedback and recommendations on their modelling strategies and decisions, particularly in large classroom settings which hinders their learning. Certainly, there is an opportunity to apply Artificial Intelligence (AI) techniques to an MDE learning environment to empower the provisioning of automated and intelligent modelling advocacy. In this paper, we propose a framework called ModBud (a modelling buddy) to educate novice modellers about the art of abstraction. ModBud uses natural language processing (NLP) and machine learning (ML) to create modelling bots with the aim of improving the modelling skills of novice modellers and assisting other practitioners, too. These bots could be used to support teaching with automatic creation or grading of models and enhance learning beyond the traditional classroom-based MDE education with timely feedback and personalized tutoring. Research challenges for the proposed framework are discussed and a research roadmap is presented.
2019-09-01
2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (publié)
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for mo… (voir plus)dern machine learning systems. In particular the online or "single-pass through the data" setting has gained attention recently as a natural setting that is difficult to tackle. Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks. These approaches typically rely on randomly selecting samples from the replay memory or from a generative model, which is suboptimal. In this work, we consider a controlled sampling of memories for replay. We retrieve the samples which are most interfered, i.e. whose prediction will be most negatively impacted by the foreseen parameters update. We show a formulation for this sampling criterion in both the generative replay and the experience replay setting, producing consistent gains in performance and greatly reduced forgetting. We release an implementation of our method at this https URL.
Much human and computational effort has aimed to improve how deep reinforcement learning (DRL) algorithms perform on benchmarks such as the … (voir plus)Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of DRL algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running DRL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous DRL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several DRL algorithms, highlighting interesting and previously unknown distinctions between them.
2019-08-10
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (publié)
This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We st… (voir plus)udy the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Aram-Alexandre Pooladian
Chris J. Finlay
Tim Hoheisel
Adam M. Oberman
Deep neural networks perform well on real world data but are prone to adversarial perturbations: small changes in the input easily lead to m… (voir plus)isclassification. In this work, we propose an attack methodology not only for cases where the perturbations are measured by
Batch normalization has been widely used to improve optimization in deep neural networks. While the uncertainty in batch statistics can act … (voir plus)as a regularizer, using these dataset statistics specific to the training set impairs generalization in certain tasks. Recently, alternative methods for normalizing feature activations in neural networks have been proposed. Among them, group normalization has been shown to yield similar, in some domains even superior performance to batch normalization. All these methods utilize a learned affine transformation after the normalization operation to increase representational power. Methods used in conditional computation define the parameters of these transformations as learnable functions of conditioning information. In this work, we study whether and where the conditional formulation of group normalization can improve generalization compared to conditional batch normalization. We evaluate performances on the tasks of visual question answering, few-shot learning, and conditional image generation.
In this work, we investigate the performance of untrained randomly initialized encoders in a general class of sequence to sequence models an… (voir plus)d compare their performance with that of fully-trained encoders on the task of abstractive summarization. We hypothesize that random projections of an input text have enough representational power to encode the hierarchical structure of sentences and semantics of documents. Using a trained decoder to produce abstractive text summaries, we empirically demonstrate that architectures with untrained randomly initialized encoders perform competitively with respect to the equivalent architectures with fully-trained encoders. We further find that the capacity of the encoder not only improves overall model generalization but also closes the performance gap between untrained randomly initialized and full-trained encoders. To our knowledge, it is the first time that general sequence to sequence models with attention are assessed for trained and randomly projected representations on abstractive summarization.
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this p… (voir plus)aper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.
2019-07-17
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
Since their introduction a year ago, distributional approaches to reinforcement learning (distributional RL) have produced strong results re… (voir plus)lative to the standard approach which models expected values (expected RL). However, aside from convergence guarantees, there have been few theoretical results investigating the reasons behind the improvements distributional RL provides. In this paper we begin the investigation into this fundamental question by analyzing the differences in the tabular, linear approximation, and non-linear approximation settings. We prove that in many realizations of the tabular and linear approximation settings, distributional RL behaves exactly the same as expected RL. In cases where the two methods behave differently, distributional RL can in fact hurt performance when it does not induce identical behaviour. We then continue with an empirical analysis comparing distributional and expected RL methods in control settings with non-linear approximators to tease apart where the improvements from distributional RL methods are coming from.
2019-07-17
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by… (voir plus) the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.
2019-07-17
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
