Learning from Learning Machines: Optimisation, Rules, and Social Norms
Travis LaCroix
There is an analogy between machine learning systems and economic entities in that they are both adaptive, and their behaviour is specified … (see more)in a more-or-less explicit way. It appears that the area of AI that is most analogous to the behaviour of economic entities is that of morally good decision-making, but it is an open question as to how precisely moral behaviour can be achieved in an AI system. This paper explores the analogy between these two complex systems, and we suggest that a clearer understanding of this apparent analogy may help us forward in both the socio-economic domain and the AI domain: known results in economics may help inform feasible solutions in AI safety, but also known results in AI may inform economic policy. If this claim is correct, then the recent successes of deep learning for AI suggest that more implicit specifications work better than explicit ones for solving such problems.
CLOSURE: Assessing Systematic Generalization of CLEVR Models
Harm de Vries
Shikhar Murty
Philippe Beaudoin
Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains
Florence Baillin
Aline Lefebvre
Amandine Pedoux
Yann Beauxis
Denis-Alexander Engemann
Anna Maruani
Frederique Amsellem
Thomas Bourgeron
Richard Delorme
Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia
Clara A. Moreau
Sebastian Urchs
Pierre Orban
Catherine Schramm
Aurélie Labbe
Guillaume Huguet
Elise Douard
Pierre-Olivier Quirion
Amy Lin
Leila Kushan
Stephanie Grot
David Luck
Adrianna Mendrek
Stephane Potvin
Emmanuel Stip
Thomas Bourgeron
Alan C. Evans
Carrie E. Bearden
Sébastien Jacquemont
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (see more)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibited worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
Applying Knowledge Transfer for Water Body Segmentation in Peru
Jessenia Gonzalez
Debjani Bhowmick
César Beltrán
Kris Sankaran
Approximate information state for partially observed systems
Jayakumar Subramanian
The standard approach for modeling partially observed systems is to model them as partially observable Markov decision processes (POMDPs) an… (see more)d obtain a dynamic program in terms of a belief state. The belief state formulation works well for planning but is not ideal for online reinforcement learning because the belief state depends on the model and, as such, is not observable when the model is unknown.In this paper, we present an alternative notion of an information state for obtaining a dynamic program in partially observed models. In particular, an information state is a sufficient statistic for the current reward which evolves in a controlled Markov manner. We show that such an information state leads to a dynamic programming decomposition. Then we present a notion of an approximate information state and present an approximate dynamic program based on the approximate information state. Approximate information state is defined in terms of properties that can be estimated using sampled trajectories. Therefore, they provide a constructive method for reinforcement learning in partially observed systems. We present one such construction and show that it performs better than the state of the art for three benchmark models.
Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with Generative Adversarial Neural Networks
T. Yuan
H. Song
David Hall
Victor Schmidt
Kris Sankaran
Automated curriculum generation for Policy Gradients from Demonstrations
Anirudh Srinivasan
Maxime Chevalier-Boisvert
Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh M. Adhikary
Siddarth Srinivasan
Byron Boots
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (see more)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model
Lina M. Tran
Sheena A. Josselyn
Paul W. Frankland
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Müller
Gonzalo Muñoz
Ambros Gleixner
Andrea Lodi
Felipe Serrano
Networked control of coupled subsystems: Spectral decomposition and low-dimensional solutions
Shuang Gao
In this paper, we investigate optimal networked control of coupled subsystems where the dynamics and the cost couplings depend on an underly… (see more)ing weighted graph. We use the spectral decomposition of the graph adjacency matrix to decompose the overall system into (L+1) systems with decoupled dynamics and cost, where L is the rank of the adjacency matrix. Consequently, the optimal control input at each subsystem can be computed by solving (L+1) decoupled Riccati equations. A salient feature of the result is that the solution complexity depends on the rank of the adjacency matrix rather than the size of the network (i.e., the number of nodes). Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale systems.