Tuan Anh Le | DeepAI

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∙ 08/21/2023

Neural Amortized Inference for Nested Multi-agent Reasoning

Multi-agent interactions, such as communication, teaching, and bluffing,...

0 Kunal Jha, et al. ∙

∙ 08/18/2023

A Rank-One Optimization Framework and Its Applications to Transmit Beamforming

This paper proposes an elegant optimization framework consisting of a mi...

0 Tuan Anh Le, et al. ∙

∙ 06/28/2023

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality...

0 Phuong L. Vo, et al. ∙

∙ 02/20/2023

Arena-Rosnav 2.0: A Development and Benchmarking Platform for Robot Navigation in Highly Dynamic Environments

Following up on our previous works, in this paper, we present Arena-Rosn...

0 Linh Kästner, et al. ∙

∙ 10/27/2022

ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images

The problem of inferring object shape from a single 2D image is undercon...

1 Matthew D. Hoffman, et al. ∙

∙ 06/12/2022

Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in Highly Dynamic Environments

The ability to autonomously navigate safely, especially within dynamic e...

0 Linh Kästner, et al. ∙

∙ 06/03/2022

Drawing out of Distribution with Neuro-Symbolic Generative Models

Learning general-purpose representations from perceptual inputs is a hal...

17 Yichao Liang, et al. ∙

∙ 04/16/2021

Learning Evolved Combinatorial Symbols with a Neuro-symbolic Generative Model

Humans have the ability to rapidly understand rich combinatorial concept...

0 Matthias Hofer, et al. ∙

∙ 07/06/2020

Learning to learn generative programs with Memoised Wake-Sleep

We study a class of neuro-symbolic generative models in which neural net...

0 Luke B. Hewitt, et al. ∙

∙ 06/30/2020

Semi-supervised Sequential Generative Models

We introduce a novel objective for training deep generative time-series ...

10 Michael Teng, et al. ∙

∙ 11/04/2019

Amortized Population Gibbs Samplers with Neural Sufficient Statistics

We develop amortized population Gibbs (APG) samplers, a new class of aut...

7 Hao Wu, et al. ∙

∙ 06/28/2019

The Thermodynamic Variational Objective

We introduce the thermodynamic variational objective (TVO) for learning ...

2 Vaden Masrani, et al. ∙

∙ 03/12/2019

Imitation Learning of Factored Multi-agent Reactive Models

We apply recent advances in deep generative modeling to the task of imit...

11 Michael Teng, et al. ∙

∙ 06/06/2018

Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observ...

2 Maximilian Igl, et al. ∙

∙ 05/26/2018

Revisiting Reweighted Wake-Sleep

Discrete latent-variable models, while applicable in a variety of settin...

0 Tuan Anh Le, et al. ∙

∙ 03/13/2018

Secure Wireless Powered and Cooperative Jamming D2D Communications

This paper investigates a secure wireless-powered device-to-device (D2D)...

0 Zheng Chu, et al. ∙

∙ 03/04/2018

A Successive Optimization Approach to Pilot Design for Multi-Cell Massive MIMO Systems

In this letter, we introduce a novel pilot design approach that minimize...

0 Hayder Al-Salihi, et al. ∙

∙ 02/20/2018

Distributed Power Control in Downlink Cellular Massive MIMO Systems

This paper compares centralized and distributed methods to solve the pow...

0 Trinh Van Chien, et al. ∙

∙ 02/13/2018

Tighter Variational Bounds are Not Necessarily Better

We provide theoretical and empirical evidence that using tighter evidenc...

0 Tom Rainforth, et al. ∙

∙ 12/21/2017

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...

0 Mario Lezcano Casado, et al. ∙

∙ 07/13/2017

Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a po...

0 Tom Rainforth, et al. ∙

∙ 05/29/2017

Auto-Encoding Sequential Monte Carlo

We introduce AESMC: a method for using deep neural networks for simultan...

0 Tuan Anh Le, et al. ∙

∙ 03/02/2017

Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to opt...

0 Tuan Anh Le, et al. ∙

∙ 10/31/2016

Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cos...

0 Tuan Anh Le, et al. ∙

∙ 12/14/2015

Data-driven Sequential Monte Carlo in Probabilistic Programming

Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...

0 Yura N Perov, et al. ∙

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