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Found 15 papers, 12 papers with code
Towards the Reusability and Compositionality of Causal Representations
Causal Representation Learning (CRL) aims at identifying high-level causal factors and their relationships from high-dimensional observations, e. g., images.
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
In this work, we propose $\verb|fit-a-nef|$, a JAX-based library that leverages parallelization to enable fast optimization of large-scale NeF datasets, resulting in a significant speed-up.
BISCUIT: Causal Representation Learning from Binary Interactions
Identifying the causal variables of an environment and how to intervene on them is of core value in applications such as robotics and embodied AI.
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice.
Differentiable Mathematical Programming for Object-Centric Representation Learning
To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation.
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems
To address this issue, we propose iCITRIS, a causal representation learning method that allows for instantaneous effects in intervened temporal sequences when intervention targets can be observed, e. g., as actions of an agent.
Complex-Valued Autoencoders for Object Discovery
Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings.
Weakly supervised causal representation learning
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone.
CITRIS: Causal Identifiability from Temporal Intervened Sequences
Understanding the latent causal factors of a dynamical system from visual observations is considered a crucial step towards agents reasoning in complex environments.
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD.
Efficient Neural Causal Discovery without Acyclicity Constraints
Learning the structure of a causal graphical model using both observational and interventional data is a fundamental problem in many scientific fields.
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing
Meta-learning, or learning to learn, is a technique that can help to overcome resource scarcity in cross-lingual NLP problems, by enabling fast adaptation to new tasks.
A Multimodal Framework for the Detection of Hateful Memes
An increasingly common expression of online hate speech is multimodal in nature and comes in the form of memes.
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided Paraphrasing
Instead of generating a response from scratch, P2-Net generates system responses by paraphrasing template-based responses.
Categorical Normalizing Flows via Continuous Transformations
Based on Categorical Normalizing Flows, we propose GraphCNF a permutation-invariant generative model on graphs.
