/main page/Seminars
 This page presents work in progress research seminars that are held weekly. These seminars carry on Machine Learning and Bayesian
 based methods applied to civil engineering. All seminars are open to the public.

 Previous seminars : 2016-2017 |
2017-2018 | 2018-2019 | 2019-2020 | 2020-2021

Jan 21 2022
  15:00 | Zoom

Presenter: James-A. Goulet | Professor, Polytechnique Montreal
Title: Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Abstract: With few exceptions, neural networks have been relying on backpropagation and gradient descent as the inference engine in order to learn the model parameters, because closed-form Bayesian inference for neural networks has been considered to be intractable. In this paper, we show how we can leverage the tractable approximate Gaussian inference's (TAGI) capabilities to infer hidden states, rather than only using it for inferring the network's parameters. One novel aspect is that it allows inferring hidden states through the imposition of constraints designed to achieve specific objectives, as illustrated through three examples: (1) the generation of adversarial-attack examples, (2) the usage of a neural network as a black-box optimization method, and (3) the application of inference on continuous-action reinforcement learning. In these three examples, the constrains are in (1), a target label chosen to fool a neural network, and in (2 & 3) the derivative of the network with respect to its input that is set to zero in order to infer the optimal input values that are either maximizing or minimizing it. These applications showcase how tasks that were previously reserved to gradient-based optimization approaches can now be approached with analytically tractable inference.

Jan 14 2022
  15:00 | Zoom

Presenter: James-A. Goulet | Professor, Polytechnique Montreal
Title: Jira: What? Why? How?
Abstract: In this special seminar, I will present the Jira management tool. I will explain what is it, why it will be useful for us to use it and how we will leverage its capacities.

Jan 6 2022
  09:00 | Zoom

Presenter: Shervin Khazael | Ph.D student, Polytechnique Montreal
Title: Can the agent accomplish the 'task' in hand?
Abstract: In the context of the Reinforcement Learning (RL), we let the agent to learn the 'task' in hand by interacting with the environment. The goodness of accomplishing the task is determined by a notion of a scalar signal known as 'reward': encourage the agent when it does good and/or penalize it when it does bad. In other words, we express the task in the form of reward values. But, how much expressive are the rewards? Answering this question depends on understanding different accounts of the 'task'. In this seminar we will discuss different tasks and argue that not all of the tasks are expressible by the rewards.