Learning Causal Structures using Hidden Compact Representation

2021 ◽  
Author(s):  
Jie Qiao ◽  
Yiming Bai ◽  
Ruichu Cai ◽  
Zhifeng Hao
Keyword(s):  
Compact Representation ◽  
Causal Structures

Interacting Aliens: Inferring Causal Structures From Covariational Data

2007 ◽  
Author(s):  
Ralf Mayrhofer ◽  
Michael R. Waldmann ◽  
York Hagmayer
Keyword(s):  
Causal Structures

The orbitofrontal cartographer

2020 ◽  
Author(s):  
Matthew P H Gardner ◽  
Geoffrey Schoenbaum
Keyword(s):  
Orbitofrontal Cortex ◽  
Economic Value ◽  
Cognitive Map ◽  
Credit Assignment ◽  
General Consensus ◽  
Future Events ◽  
Causal Structures ◽  
Precise Content ◽  
Successes And Failures

Theories of orbitofrontal cortex (OFC) function have evolved substantially over the last few decades. There is now a general consensus that the OFC is important for predicting aspects of future events and for using these predictions to guide behavior. Yet the precise content of these predictions and the degree to which OFC contributes to agency contingent upon them has become contentious, with several plausible theories advocating different answers to these questions. In this review we will focus on three of these ideas - the economic value, credit assignment, and cognitive map hypotheses – describing both their successes and failures. We will propose that these failures hint at a more nuanced role for the OFC in supporting the proposed functions when an underlying model or map of the causal structures in the environment must be constructed or updated.

scholarly journals Method for designing multi-input system identification signals using a compact time-frequency representation

2021 ◽  
Author(s):  
Mathias Stefan Roeser ◽  
Nicolas Fezans
Keyword(s):  
System Identification ◽  
Design Method ◽  
Flight Test ◽  
Compact Representation ◽  
Time Frequency ◽  
Stability And Control ◽  
Frequency Representation ◽  
Aerodynamic Parameters ◽  
Definition Of ◽  
And Control

AbstractA flight test campaign for system identification is a costly and time-consuming task. Models derived from wind tunnel experiments and CFD calculations must be validated and/or updated with flight data to match the real aircraft stability and control characteristics. Classical maneuvers for system identification are mostly one-surface-at-a-time inputs and need to be performed several times at each flight condition. Various methods for defining very rich multi-axis maneuvers, for instance based on multisine/sum of sines signals, already exist. A new design method based on the wavelet transform allowing the definition of multi-axis inputs in the time-frequency domain has been developed. The compact representation chosen allows the user to define fairly complex maneuvers with very few parameters. This method is demonstrated using simulated flight test data from a high-quality Airbus A320 dynamic model. System identification is then performed with this data, and the results show that aerodynamic parameters can still be accurately estimated from these fairly simple multi-axis maneuvers.

scholarly journals Conditional out-of-distribution generation for unpaired data using transfer VAE

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i610-i617
Author(s):  
Mohammad Lotfollahi ◽  
Mohsen Naghipourfar ◽  
Fabian J Theis ◽  
F Alexander Wolf
Keyword(s):  
Single Cell ◽  
Generative Models ◽  
Response To Treatment ◽  
High Dimensional ◽  
Compact Representation ◽  
Hair Color ◽  
Great Success ◽  
Cell Type ◽  
Style Transfer ◽  
Cell Type Specific

Abstract Motivation While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. Results We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. Availability and implementation The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals The Inflation Technique Completely Solves the Causal Compatibility Problem

2020 ◽  
Vol 8 (1) ◽  
pp. 70-91 ◽  
Author(s):  
Miguel Navascués ◽  
Elie Wolfe
Keyword(s):  
Linear Programming ◽  
Latent Variables ◽  
Causal Explanation ◽  
Causal Structure ◽  
Categorical Variables ◽  
False Negatives ◽  
The Given ◽  
Causal Structures ◽  
Compatibility Question ◽  
Structure Graph

AbstractThe causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the nth-order inflation test must be $\begin{array}{} \displaystyle {O}{\left(n^{{{-}{1}}/{2}}\right)} \end{array}$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.

Hardware-Accelerated Dual-Split Trees

2020 ◽  
Vol 3 (2) ◽  
pp. 1-21
Author(s):  
Daqi Lin ◽  
Elena Vasiou ◽  
Cem Yuksel ◽  
Daniel Kopta ◽  
Erik Brunvand
Keyword(s):  
Ray Tracing ◽  
Hardware Acceleration ◽  
Memory Storage ◽  
Compact Representation ◽  
Space Partitioning ◽  
Data Movement ◽  
Bounding Volume ◽  
Bounding Boxes ◽  
Split Trees ◽  
Bounding Volume Hierarchies

Bounding volume hierarchies (BVH) are the most widely used acceleration structures for ray tracing due to their high construction and traversal performance. However, the bounding planes shared between parent and children bounding boxes is an inherent storage redundancy that limits further improvement in performance due to the memory cost of reading these redundant planes. Dual-split trees can create identical space partitioning as BVHs, but in a compact form using less memory by eliminating the redundancies of the BVH structure representation. This reduction in memory storage and data movement translates to faster ray traversal and better energy efficiency. Yet, the performance benefits of dual-split trees are undermined by the processing required to extract the necessary information from their compact representation. This involves bit manipulations and branching instructions which are inefficient in software. We introduce hardware acceleration for dual-split trees and show that the performance advantages over BVHs are emphasized in a hardware ray tracing context that can take advantage of such acceleration. We provide details on how the operations needed for decoding dual-split tree nodes can be implemented in hardware and present experiments in a number of scenes with different sizes using path tracing. In our experiments, we have observed up to 31% reduction in render time and 38% energy saving using dual-split trees as compared to binary BVHs representing identical space partitioning.

Learning Causal Structures Based on Divide and Conquer

2020 ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Shuigeng Zhou ◽  
Chuanxu Yan ◽  
Jihong Guan ◽  
Xin Wang ◽  
...  
Keyword(s):  
Divide And Conquer ◽  
Causal Structures
Sign in / Sign up

Export Citation Format

Share Document