Results 51 to 60 of about 53,216 (300)
Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models
Gazi Üniversitesi Fen Bilimleri DergisiThe Earth's internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis.
Seda Şahin , Emine Çankaya
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AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING [PDF]
Applied Computer Science, 2019 This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon.
Saheed ADEWUYI +4 more
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2017 International Joint Conference on Neural Networks (IJCNN), 2017 In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time.
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Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm
CoRR, 2019 Due to their prevalence, time series forecasting is crucial in multiple domains. We seek to make state-of-the-art forecasting fast, accessible, and generalizable. ES-RNN is a hybrid between classical state space forecasting models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4 competition. Crucially, ES-RNN implementation requires per-
Andrew Redd, Kaung Khin, Aldo Marini
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Pushing the limits of RNN Compression [PDF]
2019 Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing - NeurIPS Edition (EMC2-NIPS), 2019 Recurrent Neural Networks (RNN) can be difficult to deploy on resource constrained devices due to their size. As a result, there is a need for compression techniques that can significantly compress RNNs without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker ...
Urmish Thakker +6 more
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On‐Chip Photonic Neural Network Architectures
Advanced Optical Materials, EarlyView.This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong +7 more
wiley +1 more source
Few-Shot-BERT-RNN Narrative Structure Analysis for Andersen's Stories
JOIV: International Journal on Informatics VisualizationEvent Extraction (EE) is a pivotal task for NLP, where important events in the narrative text need to be detected and recognized. We present an alternative method for extracting events from Hans Christian Andersen's fairy tales, utilizing Few-Shot ...
Erna Daniati +3 more
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MAM-RNN: Multi-level attention model based RNN for video captioning
, 2017 Visual information is quite important for the task of video captioning. However, in the video, there are a lot of uncorrelated content, which may cause interference to generate a correct caption.
Lu, Xiaoqiang +5 more
core +1 more source