Yitao Liang | DeepAI

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∙ 05/24/2023

Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners

The emergent few-shot reasoning capabilities of Large Language Models (L...

0 Xiaojuan Tang, et al. ∙

∙ 02/16/2023

Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits

Probabilistic Circuits (PCs) are a general and unified computational fra...

0 Xuejie Liu, et al. ∙

∙ 02/03/2023

Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents

In this paper, we study the problem of planning in Minecraft, a popular,...

0 Zihao Wang, et al. ∙

∙ 01/21/2023

Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon Prediction

We study the problem of learning goal-conditioned policies in Minecraft,...

0 Shaofei Cai, et al. ∙

∙ 11/20/2022

Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation

Learning new task-specific skills from a few trials is a fundamental cha...

0 Zhizhou Ren, et al. ∙

∙ 10/24/2022

RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding

Knowledge graph (KG) reasoning is an important problem for knowledge gra...

0 Xiaojuan Tang, et al. ∙

∙ 10/14/2022

SQA3D: Situated Question Answering in 3D Scenes

We propose a new task to benchmark scene understanding of embodied agent...

13 Xiaojian Ma, et al. ∙

∙ 07/16/2021

Towards an Interpretable Latent Space in Structured Models for Video Prediction

We focus on the task of future frame prediction in video governed by und...

8 Rushil Gupta, et al. ∙

∙ 06/29/2020

Handling Missing Data in Decision Trees: A Probabilistic Approach

Decision trees are a popular family of models due to their attractive pr...

13 Pasha Khosravi, et al. ∙

∙ 06/15/2020

On Effective Parallelization of Monte Carlo Tree Search

Despite its groundbreaking success in Go and computer games, Monte Carlo...

0 Anji Liu, et al. ∙

∙ 02/25/2020

Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration

Off-policy reinforcement learning (RL) is concerned with learning a rewa...

13 Anji Liu, et al. ∙

∙ 12/06/2019

LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning

We describe a policy learning approach to map visual inputs to driving c...

19 Albert Zhao, et al. ∙

∙ 10/05/2019

On Tractable Computation of Expected Predictions

Computing expected predictions has many interesting applications in area...

15 Pasha Khosravi, et al. ∙

∙ 03/05/2019

What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features

While discriminative classifiers often yield strong predictive performan...

17 Pasha Khosravi, et al. ∙

∙ 02/27/2019

Learning Logistic Circuits

This paper proposes a new classification model called logistic circuits....

8 Yitao Liang, et al. ∙

∙ 11/01/2018

Horizon: Facebook's Open Source Applied Reinforcement Learning Platform

In this paper we present Horizon, Facebook's open source applied reinfor...

0 Jason Gauci, et al. ∙

∙ 06/01/2018

Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing

Incentive mechanisms for crowdsourcing are designed to incentivize finan...

0 Zehong Hu, et al. ∙

∙ 11/29/2017

A Semantic Loss Function for Deep Learning with Symbolic Knowledge

This paper develops a novel methodology for using symbolic knowledge in ...

0 Jingyi Xu, et al. ∙

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