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ESMFL: Efficient and Secure Models for Federated Learning
Deep Neural Networks are widely applied to various domains. The successf...
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RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Recurrent neural networks (RNNs) based automatic speech recognition has ...
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An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and eff...
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PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
With the emergence of a spectrum of high-end mobile devices, many applic...
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A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation
The computing wall and data movement challenges of deep neural networks ...
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DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
The rapidly growing parameter volume of deep neural networks (DNNs) hind...
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Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
Deep neural networks (DNNs) have been expanded into medical fields and t...
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Learning Dynamic Context Augmentation for Global Entity Linking
Despite of the recent success of collective entity linking (EL) methods,...
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An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
The high computation and memory storage of large deep neural networks (D...
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Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
The state-of-art DNN structures involve intensive computation and high m...
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Non-structured DNN Weight Pruning Considered Harmful
Large deep neural network (DNN) models pose the key challenge to energy ...
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KCAT: A Knowledge-Constraint Typing Annotation Tool
Fine-grained Entity Typing is a tough task which suffers from noise samp...
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Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Weight quantization is one of the most important techniques of Deep Neur...
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ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
The state-of-art DNN structures involve high computation and great deman...
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Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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Learning Topics using Semantic Locality
The topic modeling discovers the latent topic probability of the given t...
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FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains...
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