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4 results for “Causal Representation Learning”
CITRIS - Causal Representation Learning Datasets
<p>This repository contains the datasets from the paper "CITRIS: Causal Identifiability from Temporal Intervened Sequences" (<a href="https://arxiv.org/abs/2202.03169">link</a>) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves.</p> <p><strong>Temporal Causal3DIdent</strong> - The Temporal Causal3DIdent dataset is a collection of 3D object shapes, which are observed under varying positions, rotations, lightning, and colors. Overall, we this dataset contains 7 (multidimensional) causal factors. The 7 shapes used are <a href="http://graphics.stanford.edu/data/3Dscanrep/">Armadillo</a>, <a href="http://graphics.stanford.edu/data/3Dscanrep/">Bunny</a>, <a href="https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/#spot">Cow</a>, <a href="http://graphics.stanford.edu/data/3Dscanrep/">Dragon</a>, <a href="https://gfx.cs.princeton.edu/proj/sugcon/models/">Head</a>, <a href="https://www.cc.gatech.edu/projects/large_models/horse.html">Horse</a>, <a href="https://github.com/brendel-group/cl-ica">Teapot</a>. We provide two versions of the dataset: one that only contains images of the Teapot, and one that uses all 7 shapes. For more details on the dataset, see <a href="https://github.com/phlippe/CITRIS">our GitHub repository</a>.</p> <p><strong>Interventional Pong</strong> - The Interventional Pong environment is inspired by the game dynamics of Pong, where both paddles follow the policy of moving towards the ball, and the ball has slightly random movements. This dataset considers the 5 causal variables paddle left, paddle right, the ball position, the ball velocity, and the score. For more details on the dataset, see <a href="https://github.com/phlippe/CITRIS">our GitHub repository</a>.</p> <p><strong>Ball-in-Boxes</strong> - The Ball-in-Boxes is a simple dataset for showcasing the concept of the minimal causal variables. The system consists of a ball which randomly moves within a box, but only under an intervention can swap between the two boxes. Thereby, the intervention does not affect the x-position in the box. Thus, one can only discover the box assignment as a causal variable, and not whether the inner x-position also belongs to it. For more details on the dataset, see <a href="https://github.com/phlippe/CITRIS">our GitHub repository</a>.</p>
iCITRIS - Causal Representation Learning Datasets
<p>This repository contains the datasets from the paper "iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects" (<a href="http://arxiv.org/abs/2206.06169">link</a>) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. </p> <p><strong>Instantaneous Temporal Causal3Ident </strong>- The Temporal Causal3DIdent dataset is a collection of 3D object shapes, which are observed under varying positions, rotations, lightning, and colors. Overall, we this dataset contains 7 (multidimensional) causal factors with instantaneous and temporal causal relations between them. The 7 shapes used are <a href="http://graphics.stanford.edu/data/3Dscanrep/">Armadillo</a>, <a href="http://graphics.stanford.edu/data/3Dscanrep/">Bunny</a>, <a href="https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/#spot">Cow</a>, <a href="http://graphics.stanford.edu/data/3Dscanrep/">Dragon</a>, <a href="https://gfx.cs.princeton.edu/proj/sugcon/models/">Head</a>, <a href="https://www.cc.gatech.edu/projects/large_models/horse.html">Horse</a>, <a href="https://github.com/brendel-group/cl-ica">Teapot</a>. For more details on the dataset, see <a href="https://github.com/phlippe/CITRIS">our GitHub repository</a>.</p> <p><strong>Causal Pinball </strong>- The Causal Pinball environment implements the simplified, real-world game dynamics of Pinball. This dataset considers 5 causal variables with instantaneous effects: the paddle position left, the paddle position right, the ball (velocity and position), the state of all bumpers, and the score. For more details on the dataset as well as the code to generate this dataset, see <a href="https://github.com/phlippe/CITRIS">our GitHub repository</a>.</p>
BISCUIT: Causal Representation Learning from Binary Interactions
<p>This repository contains the datasets from the paper "BISCUIT: Causal Representation Learning from Binary Interactions" (<a href="https://phlippe.github.io/BISCUIT/">link</a>) by Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves. </p> <p><strong>iTHOR Embodied AI </strong>- The Embodied AI dataset is generated with the iTHOR simulator. We use the default kitchen environment, FloorPlan10, and position the robot in front of the kitchen counter. The robot interacts with different objects in the environment, including a Microwave, cabinet, stove, and an egg. For more details on the dataset, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and the appendix of our paper.</p> <p><strong>CausalWorld </strong>- The CausalWorld environment implements a tri-finger robot which can interact with a cube in the center of a stage. We additionally introduce causal variables for the friction parameters of the stage, floor and cube, as well as color changes. For more details on the dataset as well as the code to generate this dataset, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and the appendix of our paper.</p> <p><strong>Voronoi</strong> - The Voronoi benchmark allows for creating causal systems with arbitrary number of causal variables and causal graphs. We provide datasets with 6 and 9 variables, as well as systems with minimal number of interactions. For details, see <a href="https://github.com/phlippe/BISCUIT">our GitHub repository</a> and our paper.</p>
Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations: SW and Data
<p>This upload contains the main simulation code and related datasets used in the conference paper entitled <a href="https://ieeexplore.ieee.org/document/10437657" target="_blank" rel="nofollow noreferrer noopener">Federated Learning Games for Reconfigurable Intelligent Surfaces via Causal Representations</a>, which was presented at <a href="https://globecom2023.ieee-globecom.org/" target="_blank" rel="nofollow noreferrer noopener">IEEE GLOBECOM 2023</a>.</p>
ScienceDex guides
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OpenNeuro
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