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Offered by Mila and the Public Policy Forum, this program is designed to equip policy and decision makers with the tools to navigate the opportunities and risks of AI. The next cohort will be held in English on November 5-6, 2026, in Ottawa.
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Publications
Step-GRAND: A Low Latency Universal Soft-Input Decoder
Syed Mohsin Abbas
Marwan Jalaleddine
Chi-Ying Tsui
Warren J. Gross
GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized … (see more)with achievable average and worst-case decoding latency. This paper introduces step-GRAND, a soft-input variant of GRAND that, in addition to achieving appealing average decoding latency, also reduces the worst-case decoding latency of the corresponding hardware implementation. The hardware implementation results demonstrate that the proposed step-GRAND can decode CA-polar code (128,105+11) with an average information throughput of 47.7 Gbps at the target FER of
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert’s behavior. However… (see more), relying solely on the expert’s data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided utilizing nonlinear dynamical systems, acting as high-level motion planners, in conjunction with the Lyapunov stability theorem. Yet, these methods are prone to inaccurate policies, high computational cost, sample inefficiency, or quasi stability when replicating complex and highly nonlinear trajectories. To mitigate this problem, we present an approach for learning a globally stable nonlinear dynamical system as a motion planning policy. We model the nonlinear dynamical system as a parametric polynomial and learn the polynomial’s coefficients jointly with a Lyapunov candidate. To showcase its success, we compare our method against the state of the art in simulation and conduct real-world experiments with the Kinova Gen3 Lite manipulator arm. Our experiments demonstrate the sample efficiency and reproduction accuracy of our method for various expert trajectories, while remaining stable in the face of perturbations.
2023-12-01
Proceedings of The 7th Conference on Robot Learning (published)
Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge
In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models.
Existing web crawl… (see more)s have demonstrated quality issues, particularly in the context of low-resource languages.
Consequently, we introduce a new multilingual pretraining corpus for
2023-11-30
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (published)
Augmenting pretrained language models with retrievers to select the supporting documents has shown promise in effectively solving common NLP… (see more) problems, including language modeling and question answering, in an interpretable way. In this paper, we first study the strengths and weaknesses of different retriever-augmented language models (REALM,
2023-11-30
Findings of the Association for Computational Linguistics: EMNLP 2023 (published)