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Found 18 papers, 0 papers with code
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes.
A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images
In our framework, by making the best use of the hardware parameters of the sensor that captures real-world space images, we first develop a high-fidelity RSO simulator that can generate various realistic space images.
Efficient 3D Object Reconstruction using Visual Transformers
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
Densely Semantic Enhancement for Domain Adaptive Region-free Detectors
Unsupervised domain adaptive object detection aims to adapt a well-trained detector from its original source domain with rich labeled data to a new target domain with unlabeled data.
A Model-driven and Data-driven Fusion Framework for Accurate Air Quality Prediction
Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations.
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling
Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years.
Fast Gated Neural Domain Adaptation: Language Model as a Case Study
We show that the neural LM perplexity can be reduced by 7. 395 and 12. 011 using the proposed domain adaptation mechanism on the Penn Treebank and News data, respectively.
ProphetMT: A Tree-based SMT-driven Controlled Language Authoring/Post-Editing Tool
This paper presents ProphetMT, a tree-based SMT-driven Controlled Language (CL) authoring and post-editing tool.
Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.
