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Found 9 papers, 2 papers with code
Convergence Analysis of Decentralized ASGD
Recently, it has been shown that the convergence of asynchronous SGD (ASGD) will always be faster than mini-batch SGD.
OPTWIN: Drift identification with optimal sub-windows
One of the main challenges of OL is the inherent presence of concept drifts, which are commonly defined as unforeseeable changes in the statistical properties of an incoming data stream over time.
Enriching Relation Extraction with OpenIE
Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses).
BigText-QA: Question Answering over a Large-Scale Hybrid Knowledge Graph
Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences.
TensAIR: Real-Time Training of Neural Networks from Data-streams
Online learning (OL) from data streams is an emerging area of research that encompasses numerous challenges from stream processing, machine learning, and networking.
Robust and Provable Guarantees for Sparse Random Embeddings
In this work, we improve upon the guarantees for sparse random embeddings, as they were recently provided and analyzed by Freksen at al. (NIPS'18) and Jagadeesan (NIPS'19).
Revisiting Initialization of Neural Networks
The proper initialization of weights is crucial for the effective training and fast convergence of deep neural networks (DNNs).
AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing
Distributed Stream Processing Systems (DSPSs) are among the currently most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics.
Distributed, Parallel, and Cluster Computing Systems and Control Systems and Control
J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features
Methods for Named Entity Recognition and Disambiguation (NERD) perform NER and NED in two separate stages.
