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 French on September 1-2, 2026, at Mila.
The Mila AI Policy Fellowship translates deep AI expertise into rigorous, public-interest policy. Read the newest publication Bridging the Expertise Gap: Knowledge Transfer Mechanisms for AI Regulation by Moritz von Knebel
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Publications
Stochastic Decoding of Linear Block Codes With High-Density Parity-Check Matrices
Saeed Sharifi Tehrani
Christophe Jego
Bo Zhu
Warren J. Gross
This correspondence extends the application of the recently proposed stochastic decoding approach to decode linear block codes with high-den… (see more)sity parity-check matrices and discusses its hardware complexity. Results demonstrate decoding performance close to floating-point iterative soft-input soft-output (SISO) decoding while offering nodes with considerably lower complexity compared to fixed-point SISO decoding.
2008-10-31
IEEE Transactions on Signal Processing (published)
Distributed Average Consensus With Dithered Quantization
Tuncer Can Aysal
Mark J. Coates
Michael G. Rabbat
In this paper, we develop algorithms for distributed computation of averages of the node data over networks with bandwidth/power constraints… (see more) or large volumes of data. Distributed averaging algorithms fail to achieve consensus when deterministic uniform quantization is adopted. We propose a distributed algorithm in which the nodes utilize probabilistically quantized information, i.e., dithered quantization, to communicate with each other. The algorithm we develop is a dynamical system that generates sequences achieving a consensus at one of the quantization values almost surely. In addition, we show that the expected value of the consensus is equal to the average of the original sensor data. We derive an upper bound on the mean-square-error performance of the probabilistically quantized distributed averaging (PQDA). Moreover, we show that the convergence of the PQDA is monotonic by studying the evolution of the minimum-length interval containing the node values. We reveal that the length of this interval is a monotonically nonincreasing function with limit zero. We also demonstrate that all the node values, in the worst case, converge to the final two quantization bins at the same rate as standard unquantized consensus. Finally, we report the results of simulations conducted to evaluate the behavior and the effectiveness of the proposed algorithm in various scenarios.
2008-09-30
IEEE Transactions on Signal Processing (published)