Publications

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Emerson F. Harkin

Peter R. Shen

Anisha Goel

Richard Naud

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (voir plus)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.

2021-07-05

Neuroscience (published)

doi.org

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Emerson F. Harkin

Peter R. Shen

Anisha Goel

Blake Richards

Richard Naud

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysica… (voir plus)l models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.

2021-07-05

Neuroscience (publié)

doi.org

Beyond variance reduction: Understanding the true impact of baselines on policy optimization

Wesley Chung

Valentin Thomas

Marlos C. Machado

Nicolas Le Roux

2021-07-01

Proceedings of the 38th International Conference on Machine Learning (publié)

proceedings.mlr.press

arxiv.org

Directional Graph Networks

Dominique Beaini

Saro Passaro

Vincent Létourneau

William Hamilton

Gabriele Corso

Pietro Lio

The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such… (voir plus) as over-smoothing. To overcome this limitation, we propose the first globally consistent anisotropic kernels for GNNs, allowing for graph convolutions that are defined according to topologicaly-derived directional flows. First, by defining a vector field in the graph, we develop a method of applying directional derivatives and smoothing by projecting node-specific messages into the field. Then, we propose the use of the Laplacian eigenvectors as such vector field. We show that the method generalizes CNNs on an

2021-07-01

Proceedings of the 38th International Conference on Machine Learning (publié)

proceedings.mlr.press

openreview.net

RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Soumyasundar Pal

Liheng Ma

Yingxue Zhang

Mark Coates

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical mod… (voir plus)els often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.

2021-07-01

Proceedings of the 38th International Conference on Machine Learning (publié)

proceedings.mlr.press

arxiv.org

Smart About Meds (SAM): a pilot randomized controlled trial of a mobile application to improve medication adherence following hospital discharge

Bettina Habib

David Buckeridge

Melissa Bustillo

Santiago Nicolas Marquez

Manish Thakur

Thai Tran

Daniala L Weir

Robyn Tamblyn

2021-07-01

JAMIA Open (publié)

doi.org

Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing

Anirudha Jitani

Aditya Mahajan

Zhongwen Zhu

Hatem Abou-Zeid

Emmanuel Thepie Fapi

Hakimeh Purmehdi

Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such a… (voir plus)s reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings — computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.

2021-06-29

ArXiv (preprint)

doi.org

arxiv.org

Measures of balance in combinatorial optimization

Philippe Olivier

Andrea Lodi

Gilles Pesant

2021-06-25

4OR (publié)

doi.org

Measures of balance in combinatorial optimization