1,984 research outputs found
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Robotic manipulation tasks, such as object rearrangement, play a crucial role
in enabling robots to interact with complex and arbitrary environments.
Existing work focuses primarily on single-level rearrangement planning and,
even if multiple levels exist, dependency relations among substructures are
geometrically simpler, like tower stacking. We propose Structural Concept
Learning (SCL), a deep learning approach that leverages graph attention
networks to perform multi-level object rearrangement planning for scenes with
structural dependency hierarchies. It is trained on a self-generated simulation
data set with intuitive structures, works for unseen scenes with an arbitrary
number of objects and higher complexity of structures, infers independent
substructures to allow for task parallelization over multiple manipulators, and
generalizes to the real world. We compare our method with a range of classical
and model-based baselines to show that our method leverages its scene
understanding to achieve better performance, flexibility, and efficiency. The
dataset, supplementary details, videos, and code implementation are available
at: https://manavkulshrestha.github.io/sclComment: Accepted to Conference on Robot Learning (CoRL) 202
Deep Causal Learning for Robotic Intelligence
This invited review discusses causal learning in the context of robotic
intelligence. The paper introduced the psychological findings on causal
learning in human cognition, then it introduced the traditional statistical
solutions on causal discovery and causal inference. The paper reviewed recent
deep causal learning algorithms with a focus on their architectures and the
benefits of using deep nets and discussed the gap between deep causal learning
and the needs of robotic intelligence
Continuous reorganization of cortical information flow in multiple sclerosis : a longitudinal fMRI effective connectivity study
Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS
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