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Acceleration in distributed sparse regression 22

Acceleration in distributed sparse regression

引用

Proceedings of the 36th International conference on neural Information processing Systems

作者： Marie Maros Gesualdo Scutari School of Industrial Engineering Purdue University

ISBN: (纸本)9781713871088

We study acceleration for distributed sparse regression in high-dimensions, which allows the parameter size to exceed and grow faster than the sample size. When applicable, existing distributed algorithms employing acceleration perform poorly in this setting, theoretically and numerically. We propose an accelerated distributed algorithm suitable for high-dimensions. The method couples a suitable instance of accelerated Nesterov's proximal gradient with consensus and gradient-tracking mechanisms, aiming at estimating locally the gradient of the empirical loss while enforcing agreement on the local estimates. Under standard assumptions on the statistical model and tuning parameters, the proposed method is proved to globally converge at linear rate to an estimate that is within the statistical precision of the model. The iteration (i.e., gradient oracle) complexity scales as O(√κ), while the communications per iteration are at most Õ(log m/(l – ρ)), where κ is the restricted condition number of the empirical loss, m is the number of agents, and ρ ∈ [0, 1) measures the network connectivity. As by-product of our design, we also report an accelerated method for high-dimensional estimation over master-worker architectures, which is of independent interest and compares favorably with existing works.

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distributed inference and fine-tuning of large language models over the internet 23

Distributed inference and fine-tuning of large language mode...

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Proceedings of the 37th International conference on neural Information processing Systems

作者： Alexander Borzunov Max Ryabinin Artem Chumachenko Dmitry Baranchuk Tim Dettmers Younes Belkada Pavel Samygin Colin Raffel HSE Univesity Yandex Neiro.ai Yandex University of Washington Hugging Face Yandex School of Data Analysis

Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using these 50B+ models requires high-end hardware, making them inaccessible to most researchers. In this work, we investigate methods for cost-efficient inference and fine-tuning of LLMs, comparing local and distributed strategies. We observe that a large enough model (50B+) can run efficiently even on geodistributed devices in a consumer-grade network. This could allow running LLM efficiently by pooling together idle compute resources of multiple research groups and volunteers. We address two open problems: (1) how to perform inference and fine-tuning reliably if any device can disconnect abruptly and (2) how to partition LLMs between devices with uneven hardware, joining and leaving at will. In order to do that, we develop special fault-tolerant inference algorithms and load-balancing protocols that automatically assign devices to maximize the total system throughput. We showcase these algorithms in PETALS — a decentralized system that runs Llama 2 (70B) and BLOOM (176B) over the Internet up to 10x faster than offloading for interactive generation. We evaluate the performance of our system in simulated conditions and a real-world setup spanning two continents.

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Bilingual Word Embedding with Sentence Combination CNN for 1-to-N Sentence Alignment 4

Bilingual Word Embedding with Sentence Combination CNN for 1...

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4th International conference on Natural Language processing and Information Retrieval (NLPIR)

作者： Ren, Xinyuan Fu, Xiangling Zhou, Xuesi Liu, Chunsheng Gao, Songfeng Peng, Lei Beijing Univ Posts & Telecommun Sch Software Engn 10 Xitucheng Rd Beijing Peoples R China Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Minist Educ 10 Xitucheng Rd Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Multimedia Signal & Intelligent Informat Proc Lab Beijing Peoples R China BUPT & Huarong Joint Iab Smart Finance Beijing 100876 Peoples R China HuaRong RongTong Beijing Technol Co Ltd Beijing 100033 Peoples R China

ISBN: (纸本)9781450377607

Sentence alignment, as one of the most active and fundamental tasks in the field of natural language processing (NLP), is usually realized in two categories of methods. One is traditional methods which are firstly proposed, the other, which are adopted later, is based on the neural network method. Presently, under the limitation that the existing mainstream data corpora are mostly in the form of 1-to-1, the alignment models with relatively good performance mainly apply to the cases of 1-to-1 sentence alignment. However, under the circumstance that a sentence contains too much information, 1-to-N sentence alignment can actually have a better effect on sentence translation tasks, compared with the 1-to-1 form, since it is more flexible and can reduce the complexity of the original sentence. As a result, we attempt to exploit neural networks with relatively good performance in the cases of 1-to-1 to fit in the cases of 1-to-N. In this paper, a novel 1-N Bilingual word Embedding with Sentence Combination CNN Improved Framework (1-NBESCC) is proposed in order to align 1-to-N sentences more precisely. Experiments show that our proposed model performs as good as the traditional methods such as BLEUALIGN in 1-to-1 situation, but much better in 1-to-N situation.

关键词： Sentence Alignment Word Embedding Convolutional neural network Deep Learning

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LSHvec: A Vector Representation of DNA Sequences Using Locality Sensitive Hashing and FastTextWord Embeddings 21

LSHvec: A Vector Representation of DNA Sequences Using Local...

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12th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Shi, Lizhen Chen, Bo Florida Polytech Univ Lakeland FL 33805 USA

ISBN: (纸本)9781450384506

Drawing from the analogy between natural language and "genomic sequence language", we explored the applicability of word embeddings in natural language processing (NLP) to represent DNA reads in Metagenomics studies. Here, k-mer is the equivalent concept of word in NLP and it has been widely used in analyzing sequence data. However, directly replacing word embedding with k-mer embedding is problematic due to two reasons: First, the number of distinct k-mers is far more than the number of different words in our vocabulary, making the model too huge to be stored in memory. Second, sequencing errors create lots of novel k-mers (noise), which significantly degrade model performance. In this work, we introduce LSHvec, a model that leverages Locality Sensitive Hashing (LSH) for k-mer encoding to overcome these challenges. After k-mers are LSH encoded, we adopt the skip-gram with negative sampling to learn k-mer embeddings. Experiments on metagenomic datasets with labels demonstrate that k-mer encoding using LSH can not only accelerate training time and reduce the memory requirements to store the model, but also achieve higher accuracy than using alternative encoding methods. We validate that LSHvec is robust on reads with high sequencing error rates and works well with any sequencing technologies. In addition, the trained low-dimensional k-mer embeddings can be potentially used for accurate metagenomic read clustering and taxonomic classification. Finally, We demonstrate the unprecedented capability of LSHvec by participating in the second round of CAMI challenges and show that LSHvec is able to handle metagenome datasets that exceed Terabytes in size through distributed training across multiple nodes.

关键词： Metagenomic Analysis neural network Word Embedding Locality Sensitive Hashing

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DeepSQA: Understanding Sensor Data via Question Answering 21

DeepSQA: Understanding Sensor Data via Question Answering

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6th ACM/IEEE International conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Xing, Tianwei Kaplan, Lance Garcia, Luis Preece, Alun Cerutti, Federico Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90095 USA CCDC Army Res Lab Adelphi MD USA Univ Southern Calif ISI Los Angeles CA 90007 USA Cardiff Univ Cardiff Wales Univ Brescia Brescia Italy

ISBN: (纸本)9781450383547

The ubiquity of mobile, wearable, and IoT devices enhances humans with a network of environmental sensors. These devices capture raw, time-series measurements of scalar physical phenomena. To transform the data into human-digestible representations, deep learning methods have enabled high-level interpretations of the opaque raw sensory data. However, interfacing models with humans requires flexibility to support the vast database of human inquiries about sensor data. Deep learning models are usually trained to perform fixed tasks, limiting the inference outputs to a predefined set of high-level labels. To enable flexible inference, we introduce DeepSQA, a generalized Sensory Question Answering (SQA) framework that aims to enable natural language questions about raw sensory data in distributed and heterogeneous IoT networks. Given a sensory data context and a natural language question about the data, the task is to provide an accurate natural language answer. In addition to the DeepSQA, we create SQA-Gen, a software framework for generating SQA datasets using labeled source sensory data, and also generate OppQA with SQA-Gen for benchmarking different SQA models. We evaluate DeepSQA across several state-of-the-art QA models and lay the foundation and challenges for future SQA research. We further provide open-source implementations of the framework, the dataset generation tool, and access to the generated dataset, to help facilitate research on the SQA problem.

关键词： Question answering neural networks Sensor data processing

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Triple-partition network: Collaborative neural network based on the ‘End Device-Edge-Cloud’

Triple-partition Network: Collaborative Neural Network based...

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IEEE conference on Wireless Communications and networking

作者： Zhipeng Gao Dong Miao Langcheng Zhao Zijia Mo Guangpeng Qi Liang Yan State key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications China Inspur Group Ltd Jinan China

The traditional centralized data processing model represented by cloud computing cannot meet the data processing requirements that are gradually tending to the edge. Therefore, a new distributed computing model coordinated by the end devices, edges and cloud has become the main development direction. However, artificial intelligence algorithms that are widely used in cloud-only approach are difficult to embed in resource-constrained distributed frameworks. To address this issue, we propose Triple-partition network, a neural network model augment with three exit points. The structure of three exit points allows to segment the traditional neural network and deploying them on the end devices, edges, and cloud. By setting up suitable exit points through the Entropy Topsis comprehensive evaluation model, part of the data can exit the network in advance to improve the efficiency of computing services. In this experiment, the classic neural networks (Alexnet, Resnet) are used to study the Triple-partition network on a state-of-art platform and show that trained Triple-partition network can greatly reduce the end-to-end latency by over 3x while achieving high accuracy.

关键词： Training Cloud computing Adaptation models Computational modeling neural networks Transforms Tools

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Encoding Following Information with Graph Attention network for Opinion Prediction

Encoding Following Information with Graph Attention Network ...

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IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Jiajian Jiang Xiaoliang Chen Yuntian Zhang Zexia Huang Xianyong Li Yajun Du School of Computer and Software Engineering Xihua University Chengdu P. R. China

Opinion prediction is an important and challenging task. Previous work often only predicts opinions based on users’ content, ignoring the influence of neighbors’ opinions on users in social networks. In this paper, we utlize Graph Attention networks to capture neighbor information for opinion prediction. Firstly, we divide users in the online social media platform into general users and online influencers by the number of their fans. Then we built the implicit link between general users by their following list, and construct the implicit network for opinion prediction. Experimental results show that the proposed model is more accurate in opinion prediction compared to many competitive baselines.

关键词： Fans Social networking (online) Computational modeling neural networks Predictive models Ubiquitous computing Encoding

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Designing a neural network Primitive for Conditional Structural Transformations 18th

Designing a Neural Network Primitive for Conditional Structu...

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18th Russian conference on Artificial Intelligence (RCAI)

作者： Demidovskij, Alexander Babkin, Eduard Higher Sch Econ Bolshaya Pecherskaya St 25-12 Nizhnii Novgorod 603005 Russia

ISBN: (纸本)9783030595357;9783030595340

Among the problems of neural network design the challenge of explicit representing conditional structural manipulations on a sub-symbolic level plays a critical role. In response to that challenge the article proposes a computationally adequate method for design of a neural network capable of performing an important group of symbolic operations on a sub-symbolic level without initial learning: extraction of elements of a given structure, conditional branching and construction of a new structure. The neural network primitive infers on distributed representations of symbolic structures and represents a proof of concept for the viability of implementation of symbolic rules in a neural pipeline for various tasks like language analysis or aggregation of linguistic assessments during the decision making process. The proposed method was practically implemented and evaluated within the Keras framework. The network designed was tested for a particular case of transforming active-passive sentences represented in parsed grammatical structures.

关键词： Tensor product representations Artificial neural networks Linguistic decision making Natural language processing

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WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras 25th

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in ...

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25th International conference on Pattern Recognition Workshops, ICPR 2020

作者： Versaci, Francesco Distributed Computing Group CRS4 Cagliari Italy

ISBN: (纸本)9783030687625

The wavelet transform is a powerful tool for performing multiscale analysis and it is a key subroutine in countless applications, from image processing to astronomy. Recently, it has extended its range of users to include the ever growing machine learning community. For a wavelet library to be efficiently adopted in this context, it needs to provide transformations which can be integrated seamlessly in already existing machine learning workflows and neural networks, being able to leverage the same libraries and run on the same hardware (e.g., CPU vs GPU) as the rest of the machine learning pipeline, without impacting training and evaluation performance. In this paper we present WaveTF, a wavelet library available as a Keras layer, which leverages TensorFlow to exploit GPU parallelism and can be used to enrich already existing machine learning workflows. To demonstrate its efficiency we compare its raw performance against other alternative libraries and finally measure the overhead it causes to the learning process when it is integrated in an already existing Convolutional neural network. © 2021, Springer Nature Switzerland AG.

关键词： Discrete wavelet transforms

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GrAPL 2021 Keynote 2: Label Propagation and Graph neural networks

GrAPL 2021 Keynote 2: Label Propagation and Graph Neural Net...

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IEEE International Symposium on Parallel and distributed processing Workshops and Phd Forum (IPDPSW)

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms - label propagation and graph neural networks - both operate by repeatedly passing information along edges, the former by passing labels and the latter by passing node features, modulated by neural networks. These two types of algorithms have largely developed separately, and there is little understanding about their relationship and how the approaches can be meaningfully combined. In this talk, I will present some probabilistic models that unify these algorithms, showing how label propagation and graph neural network ideas are naturally connected and how this leads to algorithms that can use both effectively. The talk will also discuss computational and machine learning tradeoffs of complex, highly expressive models that are expensive to train and difficult to implement, compared to simpler, less expressive models that run faster, are easy to implement, and offer more opportunities for parallelism.

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