Publications

Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study
Ali Khatibi
Shahabeddin Vahdat
Ovidiu Lungu
Jürgen Finsterbusch
Christian Büchel
Julien Cohen-Adad
Veronique Marchand-Pauvert
Julien Doyon
2021-12-31
NeuroImage (published)
doi.org
Building Together - Towards a Roadmap for African Language Technologies
Kathleen Siminyu
Jade Abbott
Kọ́lá Túbọ̀sún
Aremu Anuoluwapo
Blessing Kudzaishe Sibanda
Kofi Yeboah
David Ifeoluwa Adelani
Masabata Mokgesi-Selinga
Frederick R. Apina
Angela Thandizwe Mthembu
Arshath Ramkilowan
Babatunde Oladimeji
2021-12-31
Social Science Research Network (published)
doi.org
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Ibtihel Amara
Maryam Ziaeefard
Brett H. Meyer
Warren Gross
James J. Clark
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD… (see more) is affected by the large capacity gap between the teacher and student networks. Recent methods have resorted to a multiple teacher assistant (TA) setting for KD, which sequentially decreases the size of the teacher model to relatively bridge the size gap between these models. This paper proposes a new technique called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to efficiently enhance the learning of a compact student under the capacity gap problem. This technique is built upon the hypothesis that a student network should be guided gradually using stratified teaching curriculum as it learns easy (hard) data samples better and faster from a lower (higher) capacity teacher network. Specifically, our method is a gradual TA-based KD technique that selects a single teacher per input image based on a curriculum driven by the difficulty in classifying the image. In this work, we empirically verify our hypothesis and rigorously experiment with CIFAR-10, CIFAR-100, CINIC-10, and ImageNet datasets and show improved accuracy on VGG-like models, ResNets, and WideResNets architectures.
2021-12-31
International Conference on Pattern Recognition (published)
doi.org
arxiv.org
Cognitive Models as Simulators: The Case of Moral Decision-Making
Ardavan S. Nobandegani
T. Shultz
Irina Rish
2021-12-31
CogSci (published)
doi.org
arxiv.org
COIL: A Deep Architecture for Column Generation
Behrouz Babaki
Laurent Charlin
Sanjay Dominik Jena
. Column generation is a popular method to solve large-scale linear programs with an exponential number of variables. Several important appl… (see more)ications, such as the vehicle routing problem, rely on this technique in order to be solved. However, in practice, column generation methods suffer from slow convergence (i.e. they require too many iterations). Stabilization techniques, which carefully select the column to add at each iteration, are commonly used to improve convergence. In this work, we frame the problem of selecting which columns to add as one of sequential decision-making. We propose a neural column generation architecture that iteratively selects columns to be added to the problem. Our architecture is inspired by stabilization techniques and predicts the optimal duals, which are then used to select the columns to add. We proposed architecture, trained using imitation learning. Exemplified on the Vehicle Routing Problem, we show that several machine learning models yield good performance in predicting the optimal duals and that our architecture outperforms them as well as a popular state-of-the-art stabilization technique. Further, the architecture approach can generalize to instances larger than those observed during training.
2021-12-31
(published)
www.semanticscholar.org
Combining Reinforcement Learning and Constraint Programming for Sequence-Generation Tasks with Hard Constraints
Daphne Lafleur
A. Chandar
Gilles Pesant
While Machine Learning (ML) techniques are good at generating data similar to a dataset, they lack the capacity to enforce constraints. On t… (see more)he other hand, any solution to a Constraint Programming (CP) model satisfies its constraints but has no obligation to imitate a dataset. Yet, we sometimes need both. In this paper we borrow RL-Tuner, a Reinforcement Learning (RL) algorithm introduced to tune neural networks, as our enabling architecture to exploit the respective strengths of ML and CP. RL-Tuner maximizes the sum of a pretrained network’s learned probabilities and of manually-tuned penalties for each violated constraint. We replace the latter with outputs of a CP model representing the marginal probabilities of each value and the number of constraint violations. As was the case for the original RL-Tuner, we apply our algorithm to music generation since it is a highly-constrained domain for which CP is especially suited. We show that combining ML and CP, as opposed to using them individually, allows the agent to reflect the pretrained network while taking into account constraints, leading to melodic lines that respect both the corpus’ style and the music theory constraints.
2021-12-31
International Conference on Principles and Practice of Constraint Programming (published)
doi.org
Consistency-CAM: Towards Improved Weakly Supervised Semantic Segmentation.
Sai Rajeswar
Issam Hadj Laradji
Pau Rodríguez
David Vázquez
Aaron Courville
2021-12-31
BMVC (published)
dblp.uni-trier.de
Contrastive introspection (ConSpec) to rapidly identify invariant prototypes for success in RL
Chen Sun
Mila
Wannan Yang
Benjamin Alsbury-Nealy
Thomas Jiralerspong
Yoshua Bengio
†. BlakeRichards
Reinforcement learning (RL) algorithms have achieved notable success in recent years, but still struggle with fundamental issues in long-ter… (see more)m credit assignment. It remains difficult to learn in situations where success is contingent upon multiple critical steps that are distant in time from each other and from a sparse reward; as is often the case in real life. Moreover, how RL algorithms assign credit in these difficult situations is typically not coded in a way that can rapidly generalize to new situations. Here, we present an approach using offline contrastive learning, which we call contrastive introspection (ConSpec), that can be added to any existing RL algorithm and addresses both issues. In ConSpec, a contrastive loss is used during offline replay to identify invariances among successful episodes. This takes advantage of the fact that it is easier to retrospectively identify the small set of steps that success is contingent upon than it is to prospectively predict reward at every step taken in the environment. ConSpec stores this knowledge in a collection of prototypes summarizing the intermediate states required for success. During training, arrival at any state that matches these prototypes generates an intrinsic reward that is added to any external rewards. As well, the reward shaping provided by ConSpec can be made to preserve the optimal policy of the underlying RL agent. The prototypes in ConSpec provide two key benefits for credit assignment: (1) They enable rapid identification of all the critical states. (2) They do so in a readily interpretable manner, enabling out of distribution generalization when sensory features are altered. In summary, ConSpec is a modular system that can be added to any existing RL algorithm to improve its long-term credit assignment.
2021-12-31
(published)
www.semanticscholar.org
Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431)
Emma Frejinger
Andrea Lodi
Michele Lombardi
Neil Yorke-Smith
Machine learning’s impressive achievements in the last decade have urged many scientific communities to ask if and how the techniques deve… (see more)loped in that field to leverage data could be used to advance research in others. The combinatorial optimisation community is one of those
2021-12-31
Dagstuhl Reports (published)
doi.org
Deep Successive Subspace Learning for Data Clustering
Mohammadreza Sadeghi
Narges Armanfard
Deep clustering combines embedding and clustering together to obtain an optimal low dimensional embedding sub- space (aka latent subspace) f… (see more)or clustering, which can be more effective compared to conventional clustering approaches such as k-means. Typical deep clustering methods employ autoencoder (AE) and obtain their optimal latent space through minimizing data reconstruction loss which has no substantial connection with the clustering performance. In contrast, in this paper we propose a novel AE-based clustering scheme Deep Successive Subspace Learning (DSSL) which simultaneously minimizes weighted reconstruction and clustering losses of data points, where weights are defined based on similarity between latent representation of data points and cluster centers. DSSL obtains its optimal latent space through K (i.e. number of clusters) successive training runs where each run corresponds to an individual cluster. At each run, DSSL focuses on reconstruction and clustering of those data points that are more likely to belong to the corresponding cluster; hence, implicitly training those network parameters that have more influence on that cluster. Experimental results on benchmark datasets demonstrate that the proposed DSSL method can significantly outperform state-of-the-art clustering approaches.
2021-12-31
International Joint Conference on Neural Networks (unknown)
doi.org
Deposited in DRO : 17 January 2022 Version of attached le : Accepted Version Peer-review status of attached
Nelly Bencomo
Jin L.C. Guo
Rachel Harrison
Hans-Martin Heyn
Tim Menzies
Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowl… (see more)edge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software. Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requirements engineering think so, arguing that requirement engineering is the secret weapon for better AI and better software1. To begin, we first need a definition. What is requirements engineering or RE? RE used to be viewed as an early lifecycle activity that proceeded analysis, design, coding and testing. For safety critical applications there is certainly a pressing need to create those requirements before the coding starts (we will return to this point, later in the paper). However, in this age of DevOps and Autonomous and Self-adaptive systems, requirements can happen at many other times in a software project[15], [14]. We say that: Requirements engineering is any discussion about what to build and how to trade-off competing cost/benefits. It can happen before, during, or after runtime. 1This paper is based on the Panel “Artificial Intelligence and Requirement Engineering: Challenges and Opportunities”, which took place at the Eighth International Workshop on Artificial Intelligence and Requirements Engineering (AIRE). As shown in Table 1 and Table 2, there are many ways AI can help RE, across a broad range of SE activities. But, what about the other way around? If we add more requirements into AI, and use RE methods to get truly desired requirements, can we make better software by combining human and artificial intelligence? In our view, when integrating AI into software engineering is a co-design problem between humans, the AI model, the data required to train and validate the desired behaviour, and the hardware running the AI model, in addition to the classical software components. This means that when integrating AI, you need to know and understand the context of the system in which you want to apply your AI model to derive the necessary model requirements [17]. For example, in the arena of safety critical systems, model construction must be guided by safety requirements. one challenge for AI in RE are safety standards that base on the EN-IEC 61508 standard2. These safety standards assume that for software only systematic faults exists. Therefore, they emphasise correct processes and the creation of lifecycle artifacts to minimise systematic mistakes during both the 2Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems; for example ISO 26262 for the automotive sector or IEC 61511 for the process industry. IEEE Software (submitted) Published by the IEEE Computer Society © 2021 IEEE 1
2021-12-31
(published)
www.semanticscholar.org
Distinguishing between fake news and satire with transformers
Jwen Fai Low
Benjamin C. M. Fung
Farkhund Iqbal
Shih-Chia Huang
2021-12-31
Expert systems with applications (published)
doi.org