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Showing 1451–1475 of 2231 papers

TitleStatusHype
Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing—0
Parallel implementations of random time algorithm for chemical network stochastic simulations—0
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets—0
TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services—0
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism—0
GnetDet: Object Detection Optimized on a 224mW CNN Accelerator Chip at the Speed of 106FPS—0
Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network—0
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network—0
Effective and Scalable Clustering on Massive Attributed Graphs—0
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO—0
[Re] Can gradient clipping mitigate label noise?—0
[Re] Spatial-Adaptive Network for Single Image DenoisingCode0
[Re] Reproducibility report of "Interpretable Complex-Valued Neural Networks for Privacy Protection"Code0
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet—0
A fast method for simultaneous reconstruction and segmentation in X-ray CT application—0
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization—0
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks—0
GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar ClusteringCode0
Efficient MPI-based Communication for GPU-Accelerated Dask ApplicationsCode0
UNIT: Unifying Tensorized Instruction Compilation—0
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices—0
ZeRO-Offload: Democratizing Billion-Scale Model TrainingCode0
On the quantization of recurrent neural networks—0
Automatic Face Understanding: Recognizing Families in Photos—0
GnetSeg: Semantic Segmentation Model Optimized on a 224mW CNN Accelerator Chip at the Speed of 318FPS—0
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