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Found 10 papers, 2 papers with code
Learning and Simulation in Generative Structured World Models
The G-SWM not only unifies the key properties of previous models in a principled framework but also achieves two crucial new abilities, multi-modal uncertainty and situated behavior.
Neural Language of Thought Models
The Language of Thought Hypothesis suggests that human cognition operates on a structured, language-like system of mental representations.
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
In this paper, we investigate the effectiveness of OCR pre-training for image-based reinforcement learning via empirical experiments.
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 resolve many critical limitations of traditional single-vector representations such as poor systematic generalization.
TransDreamer: Reinforcement Learning with Transformer World Models
We then share this world model with a transformer-based policy network and obtain stability in training a transformer-based RL agent.
Model-based Reinforcement Learning
reinforcement-learning
+1
Generative Video Transformer: Can Objects be the Words?
We compare our model with previous RNN-based approaches as well as other possible video transformer baselines.
Improving Generative Imagination in Object-Centric World Models
Third, a few key abilities for more faithful temporal imagination such as multimodal uncertainty and situation-awareness are missing.
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Weight pruning is a powerful technique to realize model compression.
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes.
Blockchain as a Service: A Decentralized and Secure Computing Paradigm
Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications.
