Mila’s AI for Climate Studio aims to bridge the gap between technology and impact to unlock the potential of AI in tackling the climate crisis rapidly and on a massive scale.
The program recently published its first policy brief, titled "Policy Considerations at the Intersection of Quantum Technologies and Artificial Intelligence," authored by Padmapriya Mohan.
Hugo Larochelle appointed Scientific Director of Mila
An adjunct professor at the Université de Montréal and former head of Google's AI lab in Montréal, Hugo Larochelle is a pioneer in deep learning and one of Canada’s most respected researchers.
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The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, most deep learning arc… (see more)hitectures are fixed-resolution; they consider a single resolution at training and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. In contrast, other deep learning architectures are adaptive-resolution; they directly allow various resolutions to be processed at training and inference time. This provides computational adaptivity but either sacrifices robustness or compatibility with mainstream layers, which hinders their use. In this work, we introduce Adaptive Resolution Residual Networks (ARRNs) to surpass this tradeoff. We construct ARRNs from Laplacian residuals, which serve as generic adaptive-resolution adapters for fixed-resolution layers. We use smoothing filters within Laplacian residuals to linearly separate input signals over a series of resolution steps. We can thereby skip Laplacian residuals to cast high-resolution ARRNs into low-resolution ARRNs that are computationally cheaper yet numerically identical over low-resolution signals. We guarantee this result when Laplacian residuals are implemented with perfect smoothing kernels. We complement this novel component with Laplacian dropout, which randomly omits Laplacian residuals during training. This regularizes for robustness to a distribution of lower resolutions. This also regularizes for numerical errors that may occur when Laplacian residuals are implemented with approximate smoothing kernels. We provide a solid grounding for the advantageous properties of ARRNs through a theoretical analysis based on neural operators, and empirically show that ARRNs embrace the challenge posed by diverse resolutions with computational adaptivity, robustness, and compatibility with mainstream layers.
We introduce Adaptive Resolution Residual Networks (ARRNs), a form of neural operator that enables the creation of networks for signal-based… (see more) tasks that can be rediscretized to suit any signal resolution. ARRNs are composed of a chain of Laplacian residuals that each contain ordinary layers, which do not need to be rediscretizable for the whole network to be rediscretizable. ARRNs have the property of requiring a lower number of Laplacian residuals for exact evaluation on lower-resolution signals, which greatly reduces computational cost. ARRNs also implement Laplacian dropout, which encourages networks to become robust to low-bandwidth signals. ARRNs can thus be trained once at high-resolution and then be rediscretized on the fly at a suitable resolution with great robustness.