Search Results for author:
Found 18 papers, 7 papers with code
Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification
Our user studies confirm that the learned LEs are explainable and capture domain semantics.
Neuro-Symbolic Approaches for Text-Based Policy Learning
We outline a method for end-to-end differentiable symbolic rule learning and show that such symbolic policies outperform previous state-of-the-art methods in text-based RL for the coin collector environment from 5-10x fewer training games.
Improving Cross-lingual Text Classification with Zero-shot Instance-Weighting
In this paper, we propose zero-shot instance-weighting, a general model-agnostic zero-shot learning framework for improving CLTC by leveraging source instance weighting.
Learning variant product relationship and variation attributes from e-commerce website structures
Here, we formulate a new type of entity resolution in variant product relationships to capture these similar e-commerce product links.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Human-annotated labels and explanations are critical for training explainable NLP models.
A Closer Look at the Calibration of Differentially Private Learners
Across 7 tasks, temperature scaling and Platt scaling with DP-SGD result in an average 3. 1-fold reduction in the in-domain expected calibration error and only incur at most a minor percent drop in accuracy.
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data.
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings.
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
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.
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
We propose DIAL, a scalable active learning approach that jointly learns embeddings to maximize recall for blocking and accuracy for matching blocked pairs.
Logic Embeddings for Complex Query Answering
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.
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial Regularization
Network representation learning (NRL) is crucial in the area of graph learning.
A Survey of the State of Explainable AI for Natural Language Processing
Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable.
Forecasting in multivariate irregularly sampled time series with missing values
Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains.
A Comprehensive Benchmark Framework for Active Learning Methods in Entity Matching
Entity Matching (EM) is a core data cleaning task, aiming to identify different mentions of the same real-world entity.
HEIDL: Learning Linguistic Expressions with Deep Learning and Human-in-the-Loop
While the role of humans is increasingly recognized in machine learning community, representation of and interaction with models in current human-in-the-loop machine learning (HITL-ML) approaches are too low-level and far-removed from human's conceptual models.
Deep Learning with Apache SystemML
Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning.
