The Meat of AI: Architecture & Dataset Creation

Workshop at DigitalFUTURES 2021 We are happy to announce that the AR2IL (Architecture and Artificial Intelligence Laboratory) at Taubman College of Architecture and Urban Planning, University of Michigan, will be hosting a workshop at DigitalFUTURES this year. Have a look under the hood of the ongoing research at the AR2IL. (https://ar2il.com/) We are focussing onContinue reading “The Meat of AI: Architecture & Dataset Creation”

Paper of the Week: Cause and Effect: Concept-based Explanation of Neural Networks

05/14/2021 ∙ by Mohammad Nokhbeh Zaeem, et al. ∙ Carleton University ∙ In many scenarios, human decisions are explained based on some high-level concepts. In this work, we take a step in the interpretability of neural networks by examining their internal representation or neuron‘s activations against concepts. A concept is characterized by a set of samples that have specific features inContinue reading “Paper of the Week: Cause and Effect: Concept-based Explanation of Neural Networks”

Paper of the week

Graph Learning based Recommender Systems: A Review ecent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics for recommendations. Differently from other RS approaches, including content-based filtering and collaborative filtering,Continue reading “Paper of the week”

3D Graph Convolutional Neural Networks in Architecture Design

Dr. Matias del Campo, Alexandra Carlson, Dr. Sandra Manninger 1 INTRODUCTION This research started with a simple question: How can we train an algorithm, specifically some type of neural network, to understand and replicate the inherent sensibility of an architect? Of course, this question produces an entire plethora of follow up questions such as theContinue reading “3D Graph Convolutional Neural Networks in Architecture Design”