Search Results for author: Alexander Gray

Found 19 papers, 3 papers with code

Neuro-Symbolic Reinforcement Learning with First-Order Logic

no code implementations EMNLP 2021 Daiki Kimura, Masaki Ono, Subhajit Chaudhury, Ryosuke Kohita, Akifumi Wachi, Don Joven Agravante, Michiaki Tatsubori, Asim Munawar, Alexander Gray

Deep reinforcement learning (RL) methods often require many trials before convergence, and no direct interpretability of trained policies is provided.

text-based games

LOA: Logical Optimal Actions for Text-based Interaction Games

no code implementations ACL 2021 Daiki Kimura, Subhajit Chaudhury, Masaki Ono, Michiaki Tatsubori, Don Joven Agravante, Asim Munawar, Akifumi Wachi, Ryosuke Kohita, Alexander Gray

We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games.

text-based games

Logical Credal Networks

no code implementations25 Sep 2021 Haifeng Qian, Radu Marinescu, Alexander Gray, Debarun Bhattacharjya, Francisco Barahona, Tian Gao, Ryan Riegel, Pravinda Sahu

This paper introduces Logical Credal Networks, an expressive probabilistic logic that generalizes many prior models that combine logic and probability.

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

no code implementations16 Sep 2021 Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Francois Luus, Salim Roukos, Alexander Gray

Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.

Inductive logic programming Knowledge Base Completion

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

no code implementations ACL 2021 Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray

Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversational systems.

Entity Linking Fine-tuning +1

Reinforcement Learning with External Knowledge by using Logical Neural Networks

no code implementations3 Mar 2021 Daiki Kimura, Subhajit Chaudhury, Akifumi Wachi, Ryosuke Kohita, Asim Munawar, Michiaki Tatsubori, Alexander Gray

Specifically, we propose an integrated method that enables model-free reinforcement learning from external knowledge sources in an LNNs-based logical constrained framework such as action shielding and guide.

Logic Embeddings for Complex Query Answering

3 code implementations28 Feb 2021 Francois Luus, Prithviraj Sen, Pavan Kapanipathi, Ryan Riegel, Ndivhuwo Makondo, Thabang Lebese, Alexander Gray

Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the number of existential variables.

Knowledge Graphs Link Prediction

Foundations of Reasoning with Uncertainty via Real-valued Logics

no code implementations6 Aug 2020 Ronald Fagin, Ryan Riegel, Alexander Gray

Real-valued logics underlie an increasing number of neuro-symbolic approaches, though typically their logical inference capabilities are characterized only qualitatively.

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

no code implementations13 Dec 2019 Daniel Karl I. Weidele, Justin D. Weisz, Eno Oduor, Michael Muller, Josh Andres, Alexander Gray, Dakuo Wang

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.

AutoML Feature Engineering

An ADMM Based Framework for AutoML Pipeline Configuration

no code implementations1 May 2019 Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.

AutoML

Which Space Partitioning Tree to Use for Search?

no code implementations NeurIPS 2013 Parikshit Ram, Alexander Gray

We consider the task of nearest-neighbor search with the class of binary-space-partitioning trees, which includes kd-trees, principal axis trees and random projection trees, and try to rigorously answer the question which tree to use for nearest-neighbor search?''

Quantization

Local Support Vector Machines:Formulation and Analysis

no code implementations14 Sep 2013 Ravi Ganti, Alexander Gray

We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature.

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