Publications

Markus Anderljung

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and … (see more)recognition that existing regulations and norms in industry and academia are insufficient to ensure responsible AI development. In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, they will need to make verifiable claims to which they can be held accountable. Those outside of a given organization also need effective means of scrutinizing such claims. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems and their associated development processes, with a focus on providing evidence about the safety, security, fairness, and privacy protection of AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.

2019-12-31

arXiv (preprint)

Towards Queryable and Traceable Domain Models

Rijul Saini

Gunter Mussbacher

Jin L.C. Guo

Jörg Kienzle

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain… (see more) modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

2019-12-31

2020 IEEE 28th International Requirements Engineering Conference (RE) (published)

Towards robust and replicable sex differences in the intrinsic brain 1 function of autism 2 3

Dorothea L. Floris

José O. A. Filho

Meng-Chuan Lai

Steve

Giavasis

Marianne Oldehinkel

Maarten Mennes

Tony Charman

Julián

Tillmann

Guillaume Dumas

Christine Ecker

Flavio Dell’Acqua

Tobias Banaschewski

Carolin Moessnang

Simon Baron-Cohen

Sarah

Durston

Eva Loth

Declan Murphy … (see 4 more)

Jan K. Buitelaar

Christian Beckmann

Michael P. Milham

A. Martino

84 Background: Marked sex differences in autism prevalence accentuate the need to understand 85 the role of biological sex-related factors i… (see more)n autism. Efforts to unravel sex differences in the 86 brain organization of autism have, however, been challenged by the limited availability of 87 female data. Methods: We addressed this gap by using a large sample of males and females 88 with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 89 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects 90 of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics 91 (voxel-level Z > 3.1, cluster-level P 0.01, gaussian random field corrected). Secondary 92 analyses assessed the robustness of the results to different pre-processing approaches and 93 their replicability in two independent samples: the EU-AIMS Longitudinal European Autism 94 Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance 95 Autism Research (GENDAAR). Results: Discovery analyses in ABIDE revealed significant 96 main effects across the intrinsic functional connectivity (iFC) of the posterior cingulate 97 cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several 98 cortical regions, largely converging in the default network midline. Sex-by-diagnosis 99 interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in 100 females with autism. All findings were robust to different pre-processing steps. Replicability 101 in independent samples varied by R-fMRI measures and effects with the targeted sex-by102 diagnosis interaction being replicated in the larger of the two replication samples – EU-AIMS 103 LEAP. Limitations: Given the lack of a priori harmonization among the discovery and 104 replication datasets available to date, sample-related variation remained and may have 105 affected replicability. Conclusions: Atypical cross-hemispheric interactions are 106 neurobiologically relevant to autism. They likely result from the combination of sex107

2019-12-31

(published)

www.semanticscholar.org

Université de Montréal Balancing Signals for Semi-Supervised Sequence Learning

Ya Xu

Christopher Pal

Aaron Courville

Training recurrent neural networks (RNNs) on long sequences using backpropagation through time (BPTT) remains a fundamental challenge. It ha… (see more)s been shown that adding a local unsupervised loss term into the optimization objective makes the training of RNNs on long sequences more effective. While the importance of an unsupervised task can in principle be controlled by a coefficient in the objective function, the gradients with respect to the unsupervised loss term still influence all the hidden state dimensions, which might cause important information about the supervised task to be degraded or erased. Compared to existing semi-supervised sequence learning methods, this thesis focuses upon a traditionally overlooked mechanism – an architecture with explicitly designed private and shared hidden units designed to mitigate the detrimental influence of the auxiliary unsupervised loss over the main supervised task. We achieve this by dividing the RNN hidden space into a private space for the supervised task or a shared space for both the supervised and unsupervised tasks. We present extensive experiments with the proposed framework on several long sequence modeling benchmark datasets. Results indicate that the proposed framework can yield performance gains in RNN models where long term dependencies are notoriously challenging to deal with.

2019-12-31

(published)

www.semanticscholar.org

Unsupervised Learning of Dense Visual Representations

Pedro O. Pinheiro

Amjad Almahairi

Ryan Y. Benmalek

Florian Golemo

Aaron Courville

Contrastive self-supervised learning has emerged as a promising approach to unsupervised visual representation learning. In general, these m… (see more)ethods learn global (image-level) representations that are invariant to different views (i.e., compositions of data augmentation) of the same image. However, many visual understanding tasks require dense (pixel-level) representations. In this paper, we propose View-Agnostic Dense Representation (VADeR) for unsupervised learning of dense representations. VADeR learns pixelwise representations by forcing local features to remain constant over different viewing conditions. Specifically, this is achieved through pixel-level contrastive learning: matching features (that is, features that describes the same location of the scene on different views) should be close in an embedding space, while non-matching features should be apart. VADeR provides a natural representation for dense prediction tasks and transfers well to downstream tasks. Our method outperforms ImageNet supervised pretraining (and strong unsupervised baselines) in multiple dense prediction tasks.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

S UPPLEMENTARY M ATERIAL - L EARNING T O N AVIGATE T HE S YNTHETICALLY A CCESSIBLE C HEMICAL S PACE U SING R EINFORCEMENT L EARNING

Sai Krishna

Gottipati

B. Sattarov

Sufeng Niu

Yashaswi Pathak

Haoran Wei

Shengchao Liu

Karam M. J. Thomas

Simon R. Blackburn

Connor Wilson. Coley

Jian Tang

A. Chandar

While updating the critic network, we multiply the normal random noise vector with policy noise of 0.2 and then clip it in the range -0.2 to… (see more) 0.2. This clipped policy noise is added to the action at the next time step a′ computed by the target actor networks f and π. The actor networks (f and π networks), target critic and target actor networks are updated once every two updates to the critic network.

2019-12-31

(published)

www.semanticscholar.org

Value Preserving State-Action Abstractions

David Abel

Nathan Umbanhowar

Khimya Khetarpal

Dilip Arumugam

Doina Precup

Michael L. Littman

2019-12-31

International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Value-driven Hindsight Modelling

Arthur Guez

Fabio Viola

Theophane Weber

Lars Buesing

Steven Kapturowski

Doina Precup

David Silver

Nicolas Heess

Value estimation is a critical component of the reinforcement learning (RL) paradigm. The question of how to effectively learn predictors fo… (see more)r value from data is one of the major problems studied by the RL community, and different approaches exploit structure in the problem domain in different ways. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. In contrast, model-free methods directly leverage the quantity of interest from the future but have to compose with a potentially weak scalar signal (an estimate of the return). In this paper we develop an approach for representation learning in RL that sits in between these two extremes: we propose to learn what to model in a way that can directly help value prediction. To this end we determine which features of the future trajectory provide useful information to predict the associated return. This provides us with tractable prediction targets that are directly relevant for a task, and can thus accelerate learning of the value function. The idea can be understood as reasoning, in hindsight, about which aspects of the future observations could help past value prediction. We show how this can help dramatically even in simple policy evaluation settings. We then test our approach at scale in challenging domains, including on 57 Atari 2600 games.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

openreview.net

View-Invariant Loop Closure with Oriented Semantic Landmarks

Jimmy Li

Karim Koreitem

David Meger

Gregory Dudek

Recent work on semantic simultaneous localization and mapping (SLAM) have shown the utility of natural objects as landmarks for improving lo… (see more)calization accuracy and robustness. In this paper we present a monocular semantic SLAM system that uses object identity and inter-object geometry for view-invariant loop detection and drift correction. Our system's ability to recognize an area of the scene even under large changes in viewing direction allows it to surpass the mapping accuracy of ORB-SLAM, which uses only local appearance-based features that are not robust to large viewpoint changes. Experiments on real indoor scenes show that our method achieves mean drift reduction of 70% when compared directly to ORB-SLAM. Additionally, we propose a method for object orientation estimation, where we leverage the tracked pose of a moving camera under the SLAM setting to overcome ambiguities caused by object symmetry. This allows our SLAM system to produce geometrically detailed semantic maps with object orientation, translation, and scale.

2019-12-31

IEEE International Conference on Robotics and Automation (published)

Makesh Narsimhan Sreedhar

You could have said that instead: Improving Chatbots with Natural Language Feedback

Kun Ni

Siva Reddy

The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (see more)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.

2019-12-31

Conference on Empirical Methods in Natural Language Processing (published)

Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling

Tong Che

Ruixiang Zhang

Jascha Sohl-Dickstein

Hugo Larochelle

Liam Paull

Yuan Cao

We show that the sum of the implicit generator log-density …

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (published)

Learning from Learning Machines: Optimisation, Rules, and Social Norms

Travis LaCroix

There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified … (see more)in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.

2019-12-28

ArXiv (preprint)