Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Scalable deep learning on distributed infrastructures: Challenges, techniques, and tools
Deep Learning (DL) has had an immense success in the recent past, leading to state-of-the-
art results in various domains, such as image recognition and natural language processing …
art results in various domains, such as image recognition and natural language processing …
Performance enhancement of artificial intelligence: A survey
The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …
significant transformation across multiple industries, as it has facilitated the automation of …
Privacy preserving machine learning with homomorphic encryption and federated learning
Privacy protection has been an important concern with the great success of machine
learning. In this paper, it proposes a multi-party privacy preserving machine learning …
learning. In this paper, it proposes a multi-party privacy preserving machine learning …
Optimus: an efficient dynamic resource scheduler for deep learning clusters
Deep learning workloads are common in today's production clusters due to the proliferation
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …
of deep learning driven AI services (eg, speech recognition, machine translation). A deep …
Gaia:{Geo-Distributed} machine learning approaching {LAN} speeds
Machine learning (ML) is widely used to derive useful information from large-scale data
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …
(such as user activities, pictures, and videos) generated at increasingly rapid rates, all over …
Pipedream: Fast and efficient pipeline parallel dnn training
PipeDream is a Deep Neural Network (DNN) training system for GPUs that parallelizes
computation by pipelining execution across multiple machines. Its pipeline parallel …
computation by pipelining execution across multiple machines. Its pipeline parallel …
{HetPipe}: Enabling large {DNN} training on (whimpy) heterogeneous {GPU} clusters through integration of pipelined model parallelism and data parallelism
Deep Neural Network (DNN) models have continuously been growing in size in order to
improve the accuracy and quality of the models. Moreover, for training of large DNN models …
improve the accuracy and quality of the models. Moreover, for training of large DNN models …
HET: scaling out huge embedding model training via cache-enabled distributed framework
Embedding models have been an effective learning paradigm for high-dimensional data.
However, one open issue of embedding models is that their representations (latent factors) …
However, one open issue of embedding models is that their representations (latent factors) …
Baechi: fast device placement of machine learning graphs
Machine Learning graphs (or models) can be challenging or impossible to train when either
devices have limited memory, or the models are large. Splitting the model graph across …
devices have limited memory, or the models are large. Splitting the model graph across …
Supporting very large models using automatic dataflow graph partitioning
This paper presents Tofu, a system that partitions very large DNN models across multiple
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …
GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow …