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Found 18 papers, 6 papers with code
Chinese Grammatical Errors Diagnosis System Based on BERT at NLPTEA-2020 CGED Shared Task
The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation.
Boosting Deep Neural Network Efficiency with Dual-Module Inference
Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.
Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia
Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators.
The Study of Perceptual Training of Chinese Mandarin Tones for Monolingual Speakers of English Using Adaptive Computer Based Training Software
The study explored a new technique of phonetic tone training, which may have a positive impact on second language learning and tone training.
Empowering Distributed Training with Sparsity-driven Data Synchronization
Distributed training is the de facto standard to scale up the training of deep learning models with multiple GPUs.
Faith: An Efficient Framework for Transformer Verification on GPUs
In this paper, we propose Faith, an efficient framework for transformer verification on GPUs.
MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms
For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery.
TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs
Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e. g., e-commerce).
Towards Efficient Ansatz Architecture for Variational Quantum Algorithms
In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing.
APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
Over the years, accelerating neural networks with quantization has been widely studied.
DSXplore: Optimizing Convolutional Neural Networks via Sliding-Channel Convolutions
It also brings profound impact to improve the applicability of the compute- and memory-intensive CNNs to a broad range of applications, such as mobile devices, which are generally short of computation power and memory.
Rubik: A Hierarchical Architecture for Efficient Graph Learning
Graph convolutional network (GCN) emerges as a promising direction to learn the inductive representation in graph data commonly used in widespread applications, such as E-commerce, social networks, and knowledge graphs.
Hardware Architecture
Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks
With the increasing popularity of graph-based learning, graph neural networks (GNNs) emerge as the essential tool for gaining insights from graphs.
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers
Graph neural networks (GNNs) have achieved high performance in analyzing graph-structured data and have been widely deployed in safety-critical areas, such as finance and autonomous driving.
An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks
To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs.
SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization
With the increasing popularity of graph-based learning, Graph Neural Networks (GNNs) win lots of attention from the research and industry field because of their high accuracy.
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs
As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings).
Distributed, Parallel, and Cluster Computing
AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms
As a promising solution to boost the performance of distance-related algorithms (e. g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges.
Distributed, Parallel, and Cluster Computing Programming Languages
