Publications

Approximate minimization of weighted tree automata
Borja Balle
Guillaume Rabusseau
2022-01-01
Information and Computation (published)
doi.org
Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability
Avinash Bhat
Austin Coursey
Grace Hu
Sixian Li
Nadia Nahar
Shurui Zhou
Christian Kästner
Jin Guo
2022-01-01
(published)
doi.org
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
Leo Schwinn
Doina Precup
Bjoern Eskofier
Dario Zanca
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby de… (see more)viate from foveated biological vision. Moreover, modeling top-down attention is generally reduced to the integration of semantic features without incorporating the signal of a high-level visual tasks that have been shown to partially guide human attention. We propose the Neural Visual Attention (NeVA) algorithm to generate visual scanpaths in a top-down manner. With our method, we explore the ability of neural networks on which we impose a biologically-inspired foveated vision constraint to generate human-like scanpaths without directly training for this objective. The loss of a neural network performing a downstream visual task (i.e., classification or reconstruction) flexibly provides top-down guidance to the scanpath. Extensive experiments show that our method outperforms state-of-the-art unsupervised human attention models in terms of similarity to human scanpaths. Additionally, the flexibility of the framework allows to quantitatively investigate the role of different tasks in the generated visual behaviors. Finally, we demonstrate the superiority of the approach in a novel experiment that investigates the utility of scanpaths in real-world applications, where imperfect viewing conditions are given.
2022-01-01
Trans. Mach. Learn. Res. (published)
openreview.net
openreview.net
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Yuhan Helena Liu
Arna Ghosh
Blake Richards
Eric Todd SheaBrown
Guillaume Lajoie
openreview.net
Beyond Mahalanobis Distance for Textual OOD Detection
Pierre Colombo
Eduardo Dadalto Câmara Gomes
Guillaume Staerman
Nathan Noiry
Pablo Piantanida
openreview.net
Bisimulation metrics and norms for real-weighted automata
Borja Balle
Pascale Gourdeau
Prakash Panangaden
2022-01-01
Information and Computation (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
2022-01-01
Social Science Research Network (published)
doi.org
Cognitive Models as Simulators: The Case of Moral Decision-Making
Ardavan S. Nobandegani
T. Shultz
Irina Rish
2022-01-01
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.
2022-01-01
(published)
www.semanticscholar.org
Compositional Attention: Disentangling Search and Retrieval
Sarthak Mittal
Sharath Chandra Raparthy
Irina Rish
Yoshua Bengio
Guillaume Lajoie
Multi-head, key-value attention is the backbone of transformer-like model architectures which have proven to be widely successful in recent … (see more)years. This attention mechanism uses multiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - selection of a relevant entity from a set via query-key interaction, and (2) retrieval - extraction of relevant features from the selected entity via a value matrix. Standard attention heads learn a rigid mapping between search and retrieval. In this work, we first highlight how this static nature of the pairing can potentially: (a) lead to learning of redundant parameters in certain tasks, and (b) hinder generalization. To alleviate this problem, we propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure. The proposed mechanism disentangles search and retrieval and composes them in a dynamic, flexible and context-dependent manner. Through a series of numerical experiments, we show that it outperforms standard multi-head attention on a variety of tasks, including some out-of-distribution settings. Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed. Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval and is easy to implement in a variety of established network architectures.
2022-01-01
International Conference on Learning Representations (published)
openreview.net
openreview.net
Computing Nash equilibria for integer programming games
Margarida Carvalho
Andrea Lodi
João Pedro Pedroso
2022-01-01
European Journal of Operational Research (published)
doi.org
arxiv.org
Continual Learning In Environments With Polynomial Mixing Times
Matthew D Riemer
Sharath Chandra Raparthy
Ignacio Cases
Gopeshh Raaj 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… (see more)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 that suffer from myopic bias and stale bootstrapped estimates. To validate the proposed theory, we study the empirical scaling behavior of mixing times with respect to the number of tasks and task switching frequency for pretrained high performing policies on seven 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.
openreview.net