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Found 13 papers, 10 papers with code
COVIDLies: Detecting COVID-19 Misinformation on Social Media
The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter.
Citations Beyond Self Citations: Identifying Authors, Affiliations, and Nationalities in Scientific Papers
Our models are accurate; we identify at least one of authors, affiliations, and nationalities of held-out papers with 40. 3%, 47. 9% and 86. 0% accuracy respectively, from the top-10 guesses of our models.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
Focused on a set of formulas used in the existing datasets based on Feynman Lectures on Physics, we recreate 120 datasets to discuss the performance of symbolic regression for scientific discovery (SRSD).
SC2: Supervised Compression for Split Computing
Split computing distributes the execution of a neural network (e. g., for a classification task) between a mobile device and a more powerful edge server.
Ensemble Transformer for Efficient and Accurate Ranking Tasks: an Application to Question Answering Systems
CERBERUS consists of two components: a stack of transformer layers that is used to encode inputs, and a set of ranking heads; unlike traditional distillation technique, each of them is trained by distilling a different large transformer architecture in a way that preserves the diversity of the ensemble members.
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w. r. t.)
Supervised Compression for Resource-Constrained Edge Computing Systems
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
While knowledge distillation (transfer) has been attracting attentions from the research community, the recent development in the fields has heightened the need for reproducible studies and highly generalized frameworks to lower barriers to such high-quality, reproducible deep learning research.
Ranked #94 on
Instance Segmentation
on COCO test-dev
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.
Split Computing for Complex Object Detectors: Challenges and Preliminary Results
Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community.
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.
