Search Results for author:
Found 12 papers, 8 papers with code
FedFormer: Contextual Federation with Attention in Reinforcement Learning
We evaluate our methods on the Meta-World environment and find that our approach yields significant improvements over FedAvg and non-federated Soft Actor-Critic single-agent methods.
Iterative Edit-Based Unsupervised Sentence Simplification
We present a novel iterative, edit-based approach to unsupervised sentence simplification.
Ranked #5 on
Text Simplification
on Newsela
Online abuse detection: the value of preprocessing and neural attention models
We propose an attention-based neural network approach to detect abusive speech in online social networks.
FastFabric: Scaling Hyperledger Fabric to 20,000 Transactions per Second
Blockchain technologies are expected to make a significant impact on a variety of industries.
Distributed, Parallel, and Cluster Computing
GRS: Combining Generation and Revision in Unsupervised Sentence Simplification
We propose GRS: an unsupervised approach to sentence simplification that combines text generation and text revision.
Consentio: Managing Consent to Data Access using Permissioned Blockchains
The data management challenge we address is to ensure high throughput and low latency of endorsing data access requests and granting or revoking consent.
Distributed, Parallel, and Cluster Computing
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions.
Efficient Discovery of Approximate Order Dependencies
Order dependencies (ODs) capture relationships between ordered domains of attributes.
Databases
Climate Action During COVID-19 Recovery and Beyond: A Twitter Text Mining Study
The Coronavirus pandemic created a global crisis that prompted immediate large-scale action, including economic shutdowns and mobility restrictions.
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
We propose a system to predict harmful discussions on social media platforms.
Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content
Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images.
CREDENCE: Counterfactual Explanations for Document Ranking
Towards better explainability in the field of information retrieval, we present CREDENCE, an interactive tool capable of generating counterfactual explanations for document rankers.
