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08/28/2023
Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation
Robotics affordances, providing information about what actions can be ta...
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06/16/2023
BISCUIT: Causal Representation Learning from Binary Interactions
Identifying the causal variables of an environment and how to intervene ...
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05/28/2023
Geometric Algebra Transformers
Problems involving geometric data arise in a variety of fields, includin...
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03/22/2023
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Embodied agents operate in a structured world, often solving tasks with ...
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01/23/2023
On the Expressive Power of Geometric Graph Neural Networks
The expressive power of Graph Neural Networks (GNNs) has been studied ex...
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11/04/2022
Deconfounded Imitation Learning
Standard imitation learning can fail when the expert demonstrators have ...
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10/24/2022
A PAC-Bayesian Generalization Bound for Equivariant Networks
Equivariant networks capture the inductive bias about the symmetry of th...
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06/28/2022
Towards a Grounded Theory of Causation for Embodied AI
There exist well-developed frameworks for causal modelling, but these re...
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06/13/2022
iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects
Causal representation learning is the task of identifying the underlying...
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05/21/2022
Equivariant Mesh Attention Networks
Equivariance to symmetries has proven to be a powerful inductive bias in...
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03/30/2022
Weakly supervised causal representation learning
Learning high-level causal representations together with a causal model ...
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02/07/2022
CITRIS: Causal Identifiability from Temporal Intervened Sequences
Understanding the latent causal factors of a dynamical system from visua...
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12/21/2021
Implicit Neural Video Compression
We propose a method to compress full-resolution video sequences with imp...
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07/22/2021
Efficient Neural Causal Discovery without Acyclicity Constraints
Learning the structure of a causal graphical model using both observatio...
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04/27/2021
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science...
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12/11/2020
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
Rate-Distortion Optimized Quantization (RDOQ) has played an important ro...
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07/16/2020
Natural Graph Networks
Conventional neural message passing algorithms are invariant under permu...
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04/20/2020
Adversarial Distortion for Learned Video Compression
In this paper, we present a novel adversarial lossy video compression mo...
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03/11/2020
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
A common approach to define convolutions on meshes is to interpret them ...
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11/05/2018
A General Theory of Equivariant CNNs on Homogeneous Spaces
Group equivariant convolutional neural networks (G-CNNs) have recently e...
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07/06/2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
We present a convolutional network that is equivariant to rigid body mot...
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06/08/2018
Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the ...
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07/01/2017