Alexander Gray | DeepAI

DeepAI AI Chat

Featured Co-authors

Sijia Liu
112 publications
Mo Yu
88 publications
Niao He
45 publications
Djallel Bouneffouf
41 publications
Dakuo Wang
39 publications
Salim Roukos
30 publications
Tian Gao
28 publications
Ibrahim Abdelaziz
28 publications
Charu Aggarwal
26 publications
Pavan Kapanipathi
26 publications
Martin Wistuba
23 publications

∙ 07/05/2023

Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning

Text-based reinforcement learning agents have predominantly been neural ...

0 Subhajit Chaudhury, et al. ∙

∙ 06/18/2023

MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types

With the growing interest in large language models, the need for evaluat...

6 Keerthiram Murugesan, et al. ∙

∙ 05/31/2023

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency

We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a s...

0 Maxwell Crouse, et al. ∙

∙ 01/15/2022

A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

Knowledge Base Question Answering (KBQA) tasks that involve complex reas...

1 Sumit Neelam, et al. ∙

∙ 12/06/2021

Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks

Recent work on neuro-symbolic inductive logic programming has led to pro...

0 Prithviraj Sen, et al. ∙

∙ 10/21/2021

LOA: Logical Optimal Actions for Text-based Interaction Games

We present Logical Optimal Actions (LOA), an action decision architectur...

0 Daiki Kimura, et al. ∙

∙ 10/21/2021

Neuro-Symbolic Reinforcement Learning with First-Order Logic

Deep reinforcement learning (RL) methods often require many trials befor...

0 Daiki Kimura, et al. ∙

∙ 09/28/2021

SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases

Knowledge Base Question Answering (KBQA) tasks that in-volve complex rea...

4 Sumit Neelam, et al. ∙

∙ 09/25/2021

Logical Credal Networks

This paper introduces Logical Credal Networks, an expressive probabilist...

0 Haifeng Qian, et al. ∙

∙ 09/16/2021

Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion

Recent interest in Knowledge Base Completion (KBC) has led to a plethora...

36 Prithviraj Sen, et al. ∙

∙ 06/17/2021

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking

Entity linking (EL), the task of disambiguating mentions in text by link...

5 Hang Jiang, et al. ∙

∙ 03/03/2021

Reinforcement Learning with External Knowledge by using Logical Neural Networks

Conventional deep reinforcement learning methods are sample-inefficient ...

0 Daiki Kimura, et al. ∙

∙ 02/28/2021

Logic Embeddings for Complex Query Answering

Answering logical queries over incomplete knowledge bases is challenging...

0 Francois Luus, et al. ∙

∙ 12/03/2020

Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning

Knowledge base question answering (KBQA) is an important task in Natural...

3 Pavan Kapanipathi, et al. ∙

∙ 09/16/2020

Leveraging Semantic Parsing for Relation Linking over Knowledge Bases

Knowledgebase question answering systems are heavily dependent on relati...

0 Nandana Mihindukulasooriya, et al. ∙

∙ 08/06/2020

Foundations of Reasoning with Uncertainty via Real-valued Logics

Real-valued logics underlie an increasing number of neuro-symbolic appro...

38 Ronald Fagin, et al. ∙

∙ 06/23/2020

Logical Neural Networks

We propose a novel framework seamlessly providing key properties of both...

28 Ryan Riegel, et al. ∙

∙ 12/13/2019

AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates

Artificial Intelligence (AI) can now automate the algorithm selection, f...

0 Daniel Karl I. Weidele, et al. ∙

∙ 10/22/2019

How can AI Automate End-to-End Data Science?

Data science is labor-intensive and human experts are scarce but heavily...

0 Charu Aggarwal, et al. ∙

∙ 09/05/2019

Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI

The rapid advancement of artificial intelligence (AI) is changing our li...

0 Dakuo Wang, et al. ∙

∙ 05/01/2019

Automated Machine Learning via ADMM

We study the automated machine learning (AutoML) problem of jointly sele...

0 Sijia Liu, et al. ∙

∙ 09/14/2013

Local Support Vector Machines:Formulation and Analysis

We provide a formulation for Local Support Vector Machines (LSVMs) that ...

0 Ravi Ganti, et al. ∙

∙ 11/03/2012

Stochastic ADMM for Nonsmooth Optimization

We present a stochastic setting for optimization problems with nonsmooth...

0 Hua Ouyang, et al. ∙

∙ 05/21/2012

Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure

In this work we consider the stochastic minimization of nonsmooth convex...

0 Hua Ouyang, et al. ∙

∙ 11/08/2011

UPAL: Unbiased Pool Based Active Learning

In this paper we address the problem of pool based active learning, and ...

0 Ravi Ganti, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world