Publications

A Conceptual Framework for Representing Events Under Public Health Surveillance.
Anya Okhmatovskaia
Yannan Shen
Iris Ganser
Nigel Collier
Nicholas B. King
Zaiqiao Meng
David L. Buckeridge
Information integration across multiple event-based surveillance (EBS) systems has been shown to improve global disease surveillance in expe… (voir plus)rimental settings. In practice, however, integration does not occur due to the lack of a common conceptual framework for encoding data within EBS systems. We aim to address this gap by proposing a candidate conceptual framework for representing events and related concepts in the domain of public health surveillance.
2021-12-31
Medical Informatics Europe (publié)
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 (publié)
dblp.uni-trier.de
Continual Learning In Environments With Polynomial Mixing Times
Matthew Riemer
Sharath Chandra Raparthy
Ignacio Cases
Gopeshh Subbaraj
Maximilian Puelma Touzel
Irina Rish
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mi… (voir plus)xing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to the development of continual RL, which we call scalable MDPs, through the lens of mixing times. In particular, we theoretically establish that scalable MDPs have mixing times that scale polynomially with the size of the problem. We go on to demonstrate that polynomial mixing times present significant difficulties for existing approaches, which suffer from myopic bias and stale bootstrapped estimates. To validate our theory, we study the empirical scaling behavior of mixing times with respect to the number of tasks and task duration for high performing policies deployed across multiple Atari games. Our analysis demonstrates both that polynomial mixing times do emerge in practice and how their existence may lead to unstable learning behavior like catastrophic forgetting in continual learning settings.
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net
Continuous MDP Homomorphisms and Homomorphic Policy Gradient
Sahand Rezaei-Shoshtari
Rosie Zhao
Prakash Panangaden
David Meger
Doina Precup
Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms. In this pape… (voir plus)r, we study abstraction in the continuous-control setting. We extend the definition of MDP homomorphisms to encompass continuous actions in continuous state spaces. We derive a policy gradient theorem on the abstract MDP, which allows us to leverage approximate symmetries of the environment for policy optimization. Based on this theorem, we propose an actor-critic algorithm that is able to learn the policy and the MDP homomorphism map simultaneously, using the lax bisimulation metric. We demonstrate the effectiveness of our method on benchmark tasks in the DeepMind Control Suite. Our method's ability to utilize MDP homomorphisms for representation learning leads to improved performance when learning from pixel observations.
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net
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… (voir plus)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
(publié)
www.semanticscholar.org
CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
Aniket Didolkar
Andrii Zadaianchuk
Rabiul Awal
Maximilian Seitzer
Efstratios Gavves
Aishwarya Agrawal
Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each … (voir plus)slot captures a distinct object. Current state-of-the-art models have shown remarkable success in object discovery, particularly in complex real-world scenes, while also generalizing well to unseen domains. However, these models suffer from a key limitation: they lack controllability. Specifically, current object-centric models learn representations based on their preconceived understanding of objects and parts, without allowing user input to guide or modify which objects are represented. Introducing controllability into object-centric models could unlock a range of useful capabilities, such as enabling models to represent scenes at variable levels of granularity based on user specification. In this work, we propose a novel approach that conditions slot representations through guided decomposition, paired with a novel contrastive learning objective, to enable user-directed control over which objects are represented. Our method achieves such controllability without any mask supervision and successfully binds to user-specified objects in complex real-world scenes.
2021-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (inconnu)
doi.org
openreview.net
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… (voir plus)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 (publié)
doi.org
Data-Efficient Structured Pruning via Submodular Optimization
Marwa El Halabi
Suraj Srinivas
Simon Lacoste-Julien
Structured pruning is an effective approach for compressing large pre-trained neural networks without significantly affecting their performa… (voir plus)nce. However, most current structured pruning methods do not provide any performance guarantees, and often require fine-tuning, which makes them inapplicable in the limited-data regime. We propose a principled data-efficient structured pruning method based on submodular optimization. In particular, for a given layer, we select neurons/channels to prune and corresponding new weights for the next layer, that minimize the change in the next layer's input induced by pruning. We show that this selection problem is a weakly submodular maximization problem, thus it can be provably approximated using an efficient greedy algorithm. Our method is guaranteed to have an exponentially decreasing error between the original model and the pruned model outputs w.r.t the pruned size, under reasonable assumptions. It is also one of the few methods in the literature that uses only a limited-number of training data and no labels. Our experimental results demonstrate that our method outperforms state-of-the-art methods in the limited-data regime.
2021-12-31
Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (publié)
doi.org
openreview.net
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… (voir plus)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
(publié)
www.semanticscholar.org
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes
Louis Clouatre
Prasanna Parthasarathi
Amal Zouaq
Sarath Chandar
Providing better language tools for low-resource and endangered languages is imperative for equitable growth. Recent progress with massively… (voir plus) multilingual pretrained models has proven surprisingly effective at performing zero-shot transfer to a wide variety of languages. However, this transfer is not universal, with many languages not currently understood by multilingual approaches. It is estimated that only 72 languages possess a "small set of labeled datasets" on which we could test a model's performance, the vast majority of languages not having the resources available to simply evaluate performances on. In this work, we attempt to clarify which languages do and do not currently benefit from such transfer. To that end, we develop a general approach that requires only unlabelled text to detect which languages are not well understood by a cross-lingual model. Our approach is derived from the hypothesis that if a model's understanding is insensitive to perturbations to text in a language, it is likely to have a limited understanding of that language. We construct a cross-lingual sentence similarity task to evaluate our approach empirically on 350, primarily low-resource, languages.
2021-12-31
EMNLP (Findings) (publié)
doi.org
arxiv.org
Discrete Factorial Representations as an Abstraction for Goal Conditioned RL
Riashat Islam
Hongyu Zang
Anirudh Goyal
Alex Lamb
Kenji Kawaguchi
Xin Li
Romain Laroche
Yoshua Bengio
Remi Tachet des Combes
2021-12-31
Neural Information Processing Systems (publié)
openreview.net
openreview.net
Discrete-Valued Neural Communication in Structured Architectures Enhances Generalization
Dianbo Liu
Alex Lamb
Kenji Kawaguchi
Anirudh Goyal
Chen Sun
Michael C. Mozer
Yoshua Bengio
Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed… (voir plus) of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic and possibly sparse communication among the separate components. Here, we explore the hypothesis that restricting the transmitted information among components to discrete representations is a beneficial bottleneck. The motivating intuition is human language in which communication occurs through discrete symbols. Even though individuals have different understandings of what a "cat" is based on their specific experiences, the shared discrete token makes it possible for communication among individuals to be unimpeded by individual differences in internal representation. To discretize the values of concepts dynamically communicated among specialist components, we extend the quantization mechanism from the Vector-Quantized Variational Autoencoder to multi-headed discretization with shared codebooks and use it for discrete-valued neural communication (DVNC). Our experiments show that DVNC substantially improves systematic generalization in a variety of architectures -- transformers, modular architectures, and graph neural networks. We also show that the DVNC is robust to the choice of hyperparameters, making the method very useful in practice. Moreover, we establish a theoretical justification of our discretization process, proving that it has the ability to increase noise robustness and reduce the underlying dimensionality of the model.
2021-12-31
(publié)
doi.org
openreview.net