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07/05/2023
Facing off World Model Backbones: RNNs, Transformers, and S4
World models are a fundamental component in model-based reinforcement le...
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03/20/2023
Object-Centric Slot Diffusion
Despite remarkable recent advances, making object-centric learning work ...
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02/09/2023
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
Unsupervised object-centric representation (OCR) learning has recently d...
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11/02/2022
Neural Block-Slot Representations
In this paper, we propose a novel object-centric representation, called ...
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05/27/2022
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Unsupervised object-centric learning aims to represent the modular, comp...
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10/27/2021
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
Top-performing Model-Based Reinforcement Learning (MBRL) agents, such as...
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07/20/2021
Generative Video Transformer: Can Objects be the Words?
Transformers have been successful for many natural language processing t...
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07/19/2021
Structured World Belief for Reinforcement Learning in POMDP
Object-centric world models provide structured representation of the sce...
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10/23/2020
Generative Neurosymbolic Machines
Reconciling symbolic and distributed representations is a crucial challe...
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10/05/2020
Improving Generative Imagination in Object-Centric World Models
The remarkable recent advances in object-centric generative world models...
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06/29/2020
Robustifying Sequential Neural Processes
When tasks change over time, meta-transfer learning seeks to improve the...
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06/11/2020
Learning to Infer 3D Object Models from Images
A crucial ability of human intelligence is to build up models of individ...
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01/08/2020
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
The ability to decompose complex multi-object scenes into meaningful abs...
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10/21/2019
Generative Hierarchical Models for Parts, Objects, and Scenes
Compositional structures between parts and objects are inherent in natur...
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10/06/2019
Scalable Object-Oriented Sequential Generative Models
The main limitation of previous approaches to unsupervised sequential ob...
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10/06/2019
Neural Multisensory Scene Inference
For embodied agents to infer representations of the underlying 3D physic...
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10/02/2019
Variational Temporal Abstraction
We introduce a variational approach to learning and inference of tempora...
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06/24/2019
Sequential Neural Processes
Neural processes combine the strengths of neural networks and Gaussian p...
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04/06/2019
Reinforced Imitation in Heterogeneous Action Space
Imitation learning is an effective alternative approach to learn a polic...
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12/04/2018
Learning Single-View 3D Reconstruction with Adversarial Training
Single-view 3D shape reconstruction is an important but challenging prob...
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06/11/2018
Bayesian Model-Agnostic Meta-Learning
Learning to infer Bayesian posterior from a few-shot dataset is an impor...
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08/01/2016
A Neural Knowledge Language Model
Current language models have a significant limitation in the ability to ...
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05/24/2016
Hierarchical Memory Networks
Memory networks are neural networks with an explicit memory component th...
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03/26/2016
Pointing the Unknown Words
The problem of rare and unknown words is an important issue that can pot...
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03/22/2016
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabl...
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10/16/2015
Scalable MCMC for Mixed Membership Stochastic Blockmodels
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algo...
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03/05/2015
Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
Despite having various attractive qualities such as high prediction accu...
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06/27/2012
