D'ya like dags? a survey on structure learning and causal discovery
Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …
causal relationships from data, we need structure discovery methods. We provide a review …
A survey on hyperdimensional computing aka vector symbolic architectures, part ii: Applications, cognitive models, and challenges
This is Part II of the two-part comprehensive survey devoted to a computing framework most
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
commonly known under the names Hyperdimensional Computing and Vector Symbolic …
Emergent analogical reasoning in large language models
The recent advent of large language models has reinvigorated debate over whether human
cognitive capacities might emerge in such generic models given sufficient training data. Of …
cognitive capacities might emerge in such generic models given sufficient training data. Of …
Zero-shot text-to-image generation
Text-to-image generation has traditionally focused on finding better modeling assumptions
for training on a fixed dataset. These assumptions might involve complex architectures …
for training on a fixed dataset. These assumptions might involve complex architectures …
Toward causal representation learning
The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
Imagen editor and editbench: Advancing and evaluating text-guided image inpainting
Text-guided image editing can have a transformative impact in supporting creative
applications. A key challenge is to generate edits that are faithful to the input text prompt …
applications. A key challenge is to generate edits that are faithful to the input text prompt …
Savi++: Towards end-to-end object-centric learning from real-world videos
G Elsayed, A Mahendran… - Advances in …, 2022 - proceedings.neurips.cc
The visual world can be parsimoniously characterized in terms of distinct entities with sparse
interactions. Discovering this compositional structure in dynamic visual scenes has proven …
interactions. Discovering this compositional structure in dynamic visual scenes has proven …
Object scene representation transformer
MSM Sajjadi, D Duckworth… - Advances in …, 2022 - proceedings.neurips.cc
A compositional understanding of the world in terms of objects and their geometry in 3D
space is considered a cornerstone of human cognition. Facilitating the learning of such a …
space is considered a cornerstone of human cognition. Facilitating the learning of such a …
Conditional object-centric learning from video
Object-centric representations are a promising path toward more systematic generalization
by providing flexible abstractions upon which compositional world models can be built …
by providing flexible abstractions upon which compositional world models can be built …
Simple unsupervised object-centric learning for complex and naturalistic videos
Unsupervised object-centric learning aims to represent the modular, compositional, and
causal structure of a scene as a set of object representations and thereby promises to …
causal structure of a scene as a set of object representations and thereby promises to …