Sungjin Ahn | DeepAI

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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...

0 Fei Deng, et al. ∙

∙ 03/20/2023

Object-Centric Slot Diffusion

Despite remarkable recent advances, making object-centric learning work ...

0 Jindong Jiang, et al. ∙

∙ 02/09/2023

An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning

Unsupervised object-centric representation (OCR) learning has recently d...

0 Jaesik Yoon, et al. ∙

∙ 11/02/2022

Neural Block-Slot Representations

In this paper, we propose a novel object-centric representation, called ...

0 Gautam Singh, et al. ∙

∙ 05/27/2022

Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos

Unsupervised object-centric learning aims to represent the modular, comp...

0 Gautam Singh, et al. ∙

∙ 10/27/2021

DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations

Top-performing Model-Based Reinforcement Learning (MBRL) agents, such as...

0 Fei Deng, et al. ∙

∙ 07/20/2021

Generative Video Transformer: Can Objects be the Words?

Transformers have been successful for many natural language processing t...

1 Yi-Fu Wu, et al. ∙

∙ 07/19/2021

Structured World Belief for Reinforcement Learning in POMDP

Object-centric world models provide structured representation of the sce...

11 Gautam Singh, et al. ∙

∙ 10/23/2020

Generative Neurosymbolic Machines

Reconciling symbolic and distributed representations is a crucial challe...

0 Jindong Jiang, et al. ∙

∙ 10/05/2020

Improving Generative Imagination in Object-Centric World Models

The remarkable recent advances in object-centric generative world models...

12 Zhixuan Lin, et al. ∙

∙ 06/29/2020

Robustifying Sequential Neural Processes

When tasks change over time, meta-transfer learning seeks to improve the...

6 Jaesik Yoon, et al. ∙

∙ 06/11/2020

Learning to Infer 3D Object Models from Images

A crucial ability of human intelligence is to build up models of individ...

4 Chang Chen, et al. ∙

∙ 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...

28 Zhixuan Lin, et al. ∙

∙ 10/21/2019

Generative Hierarchical Models for Parts, Objects, and Scenes

Compositional structures between parts and objects are inherent in natur...

9 Fei Deng, et al. ∙

∙ 10/06/2019

Scalable Object-Oriented Sequential Generative Models

The main limitation of previous approaches to unsupervised sequential ob...

34 Jindong Jiang, et al. ∙

∙ 10/06/2019

Neural Multisensory Scene Inference

For embodied agents to infer representations of the underlying 3D physic...

7 Jae Hyun Lim, et al. ∙

∙ 10/02/2019

Variational Temporal Abstraction

We introduce a variational approach to learning and inference of tempora...

36 Taesup Kim, et al. ∙

∙ 06/24/2019

Sequential Neural Processes

Neural processes combine the strengths of neural networks and Gaussian p...

7 Gautam Singh, et al. ∙

∙ 04/06/2019

Reinforced Imitation in Heterogeneous Action Space

Imitation learning is an effective alternative approach to learn a polic...

14 Konrad Zolna, et al. ∙

∙ 12/04/2018

Learning Single-View 3D Reconstruction with Adversarial Training

Single-view 3D shape reconstruction is an important but challenging prob...

0 Pedro O. Pinheiro, et al. ∙

∙ 06/11/2018

Bayesian Model-Agnostic Meta-Learning

Learning to infer Bayesian posterior from a few-shot dataset is an impor...

0 Taesup Kim, et al. ∙

∙ 08/01/2016

A Neural Knowledge Language Model

Current language models have a significant limitation in the ability to ...

0 Sungjin Ahn, et al. ∙

∙ 05/24/2016

Hierarchical Memory Networks

Memory networks are neural networks with an explicit memory component th...

0 Sarath Chandar, et al. ∙

∙ 03/26/2016

Pointing the Unknown Words

The problem of rare and unknown words is an important issue that can pot...

0 Caglar Gulcehre, et al. ∙

∙ 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...

0 Iulian Vlad Serban, et al. ∙

∙ 10/16/2015

Scalable MCMC for Mixed Membership Stochastic Blockmodels

We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algo...

0 Wenzhe Li, et al. ∙

∙ 03/05/2015

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

Despite having various attractive qualities such as high prediction accu...

0 Sungjin Ahn, et al. ∙

∙ 06/27/2012

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

In this paper we address the following question: Can we approximately sa...

0 Sungjin Ahn, et al. ∙

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