Search Results for author:
Found 9 papers, 4 papers with code
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers
Recently, these models achieved great performance on tasks such as compositional visual question answering, visual grounding, and video temporal reasoning.
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute
We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Current state-of-the-art object-centric models use slots and attention-based routing for binding.
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class.
Hierarchical Relational Inference
Common-sense physical reasoning in the real world requires learning about the interactions of objects and their dynamics.
R-SQAIR: Relational Sequential Attend, Infer, Repeat
Traditional sequential multi-object attention models rely on a recurrent mechanism to infer object relations.
Discrete Deep Feature Extraction: A Theory and New Architectures
First steps towards a mathematical theory of deep convolutional neural networks for feature extraction were made---for the continuous-time case---in Mallat, 2012, and Wiatowski and B\"olcskei, 2015.
