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检索条件"任意字段=Conference on Space-Efficient Data Structures, Streams, and Algorithms"

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ReNeuIR: Reaching Efficiency in Neural Information Retrieval 22

ReNeuIR: Reaching Efficiency in Neural Information Retrieval

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45th International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Bruch, Sebastian Lucchese, Claudio Nardini, Franco Maria Pinecone New York NY 10012 USA Ca Foscary Univ Venice Venice Italy ISTI CNR Pisa Italy

ISBN: (纸本)9781450387323

Perhaps the applied nature of information retrieval research goes some way to explain the community's rich history of evaluating machine learning models holistically, understanding that efficacy matters but so does the computational cost incurred to achieve it. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in learning-to-rank. As the community adopts even more complex, neural network-based models in a wide range of applications, questions on efficiency have once again become relevant. We propose this workshop as a forum for a critical discussion of efficiency in the era of neural information retrieval, to encourage debate on the current state and future directions of research in this space, and to promote more sustainable research by identifying best practices in the development and evaluation of neural models for information retrieval.

关键词： efficiency neural IR sustainable IR retrieval ranking algorithms and data structures

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BloomWalk and CuckooWalk: Fast Random Walks Utilizing Probabilistic data Structure

BloomWalk and CuckooWalk: Fast Random Walks Utilizing Probab...

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IEEE Annual International Computer Software and Applications conference (COMPSAC)

作者： Ren Inayoshi Han Nay Aung Hiroyuki Ohsaki Graduate School of Science and Technology Kwansei Gakuin University

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Random walks are being used for target node search and graph exploration in various fields, including communications and social networking. However, frequent revisits of the random walk agent to the same node degrade the efficiency of node search and graph exploration. To address this issue and improve the efficiency of node search and graph exploration, a variety of random walk-based algorithms with history, such as self-avoiding random walk (SARW) and k-history random walk (k-History), have been developed. Although these approaches accelerate node search and graph exploration with a random walk agent, they require the agent to have a non-negligible amount of memory space to store many nodes on large-scale graphs. To address this issue, it is essential to clarify efficient memory management strategies for random walk agents. In this paper, we propose novel random walk-based algorithms called BloomWalk and CuckooWalk, in which an agent performs history-based random walks on a graph using a probabilistic data structure to record previously visited nodes in memory efficiently. Experimental results demonstrate that Bloom Walk and CuckooWalk enable efficient node search on unknown graphs even with the very limited memory capacity.

关键词： Social networking (online) Accelerated aging Memory management Software algorithms Filtering algorithms Probabilistic logic data structures

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efficient Reinforcement Learning with Prior Causal Knowledge 1

Efficient Reinforcement Learning with Prior Causal Knowledge

引用

conference on Causal Learning and Reasoning

作者： Lu, Yangyi Meisami, Amirhossein Tewari, Ambuj Univ Michigan Ann Arbor MI 48109 USA Adobe Inc San Jose CA USA

We introduce causal Markov Decision Processes (C-MDPs), a new formalism for sequential decision making which combines the standard MDP formulation with causal structures over state transition and reward functions. Many contemporary and emerging application areas such as digital healthcare and digital marketing can benefit from modeling with C-MDPs due to the causal mechanisms underlying the relationship between interventions and states/rewards. We propose the causal upper confidence bound value iteration (C-UCBVI) algorithm that exploits the causal structure in C-MDPs and improves the performance of standard reinforcement learning algorithms that do not take causal knowledge into account. We prove that C-UCBVI satisfies an (O) over tilde (HS root ZT) regret bound, where T is the the total time steps, H is the episodic horizon, and S is the cardinality of the state space. Notably, our regret bound does not scale with the size of actions/interventions (A), but only scales with a causal graph dependent quantity Z which can be exponentially smaller than A. By extending C-UCBVI to the factored MDP setting, we propose the causal factored UCBVI (CF-UCBVI) algorithm, which further reduces the regret exponentially in terms of S. Furthermore, we show that RL algorithms for linear MDP problems can also be incorporated in C-MDPs. We empirically show the benefit of our causal approaches in various settings to validate our algorithms and theoretical results.

关键词： Reinforcement learning Causality Markov Decision Process (MDP)

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Improving Matrix-vector Multiplication via Lossless Grammar-Compressed Matrices 48th

Improving Matrix-vector Multiplication via Lossless Grammar-...

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48th International conference on Very Large data Bases (VLDB)

作者： Ferragina, Paolo Manzini, Giovanni Gagie, Travis Koppl, Dominik Navarro, Gonzalo Striani, Manuel Tosoni, Francesco Univ Pisa Dept Comp Sci Pisa Italy Dalhousie Univ Fac Comp Sci Halifax NS Canada Tokyo Med & Dent Univ M&D Data Sci Ctr Tokyo Japan Univ Chile Dept Comp Sci IMFD Santiago Chile Univ Piemonte Orientale Dept Sci & Technol Innovat Vercelli Italy

As nowadays Machine Learning (ML) techniques are generating huge data collections, the problem of how to efficiently engineer their storage and operations is becoming of paramount importance. In this article we propose a new lossless compression scheme for real-valued matrices which achieves efficient performance in terms of compression ratio and time for linear-algebra operations. Experiments show that, as a compressor, our tool is clearly superior to gzip and it is usually within 20% of xz in terms of compression ratio. In addition, our compressed format supports matrix-vector multiplications in time and space proportional to the size of the compressed representation, unlike gzip and xz that require the full decompression of the compressed matrix. To our knowledge our lossless compressor is the first one achieving time and space complexities which match the theoretical limit expressed by the.. -th order statistical entropy of the input. To achieve further time/space reductions, we propose columnreordering algorithms hinging on a novel column-similarity score. Our experiments on various data sets of ML matrices show that our column reordering can yield a further reduction of up to 16% in the peak memory usage during matrix-vector multiplication. Finally, we compare our proposal against the state-of-the-art Compressed Linear Algebra (CLA) approach showing that ours runs always at least twice faster (in a multi-thread setting), and achieves better compressed space occupancy and peak memory usage. This experimentally confirms the provably effective theoretical bounds we show for our compressed-matrix approach.

关键词： Matrix algebra

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MRI Reconstruction by Completing Under-sampled K-space data with Learnable Fourier Interpolation 25th

MRI Reconstruction by Completing Under-sampled K-space Data ...

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25th International conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Ding, Qiaoqiao Zhang, Xiaoqun Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MOE LSC Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ SJTU GenSci Joint Lab Shanghai 200240 Peoples R China

ISBN: (纸本)9783031164460;9783031164453

Magnetic resonance imaging (MRI) acceleration is usually achieved by data undersampling, while reconstruction from undersampled data is a challenging ill-posed problem for data-missing and noisy measurements introduce various artifacts. In recent years, deep learning methods have been extensively studied for MRI reconstruction, and most of work treat the reconstruction problem as a denoising problem or replace the regularization subproblem with a deep neural network (DNN) in an optimization unrolling scheme. In this work, we proposed to directly complete the missing and corrupted k-space data by a specially designed interpolation deep neural networks combined with some convolution layers in both frequency and spatial domains. Specifically, for every missing and corrupted frequency, we use a K- nearest neighbors estimation with learnable weights. Then, two convolution neural networks (CNNs) are applied to regularize the data in both k-space and image space. The proposed DNN structures have clear interpretability for solving this undersampling problem. Extensive experiments on MRI reconstruction with diverse sampling patterns and ratios, under noiseless and noise settings demonstrate the accuracy of the proposed method compared to other learning based algorithms, while being computationally more efficient for both training and reconstruction processes.

关键词： MRI reconstruction Deep learning Compressed sensing Learnable interpolation

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Cluster Trellis: data structures & algorithms for Exact Inference in Hierarchical Clustering 24

Cluster Trellis: Data Structures & Algorithms for Exact Infe...

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24th International conference on Artificial Intelligence and Statistics (AISTATS)

作者： Greenberg, Craig S. Macaluso, Sebastian Monath, Nicholas Lee, Ji Ah Flaherty, Patrick Cranmer, Kyle McGregor, Andrew McCallum, Andrew Univ Massachusetts Amherst MA 01003 USA NIST Gaithersburg MD 20899 USA NYU 550 1St Ave New York NY 10003 USA

Hierarchical clustering is a fundamental task often used to discover meaningful structures in data. Due to the combinatorial number of possible hierarchical clusterings, approximate algorithms are typically used for inference. In contrast to existing methods, we present novel dynamic-programming algorithms for exact inference in hierarchical clustering based on a novel trellis data structure, and we prove that we can exactly compute the partition function, maximum likelihood hierarchy, and marginal probabilities of sub-hierarchies and clusters. Our algorithms scale in time and space proportional to the powerset of N elements, which is super-exponentially more efficient than explicitly considering each of the (2N - 3)!! possible hierarchies. Also, for larger datasets where our exact algorithms become infeasible, we introduce an approximate algorithm based on a sparse trellis that outperforms greedy and beam search baselines.

关键词： data structures

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INTEGRATION OF DESIGN data INTO AUTOMATED FIBER PLACEMENT PROCESS PLANNING METRICS

INTEGRATION OF DESIGN DATA INTO AUTOMATED FIBER PLACEMENT PR...

引用

SAMPE 2023 conference and Exhibition

作者： Brasington, Alex Halbritter, Joshua Noevere, August Harik, Ramy McNair Center University of South Carolina ColumbiaSC United States Collier Aerospace Newport News VA United States

ISBN: (纸本)9781934551431

With the ever-expanding aviation industry, a need is arising for more rapid production of composite aircraft to meet increasing demand. State-of-the-art aircraft such as the Boeing 787 showcase 50% composite material usage by weight, highlighting this emerging industry-wide adoption of the material system. Currently, many of the large structures associated with these aircraft are manufactured additively via Automated Fiber Placement (AFP). The AFP process shows great potential for efficient manufacturing, however unavoidable defects still occur because of tool surface geometry, placement errors, or poor process planning, resulting in decreased quality and throughput. Due to such effects, it is critical to incorporate design for manufacturing (DFM) principles to achieve the optimal manufacturing plan and resulting structure. This work will develop a methodology for incorporating design information into process planning metrics in an automated fashion to achieve an optimal set of process inputs. The analysis incorporates HyperX, Computer Aided Process Planning (CAPP) and Vericut Composite Programming (VCP). Safety margins from HyperX are imported into CAPP where AFP defects are mapped to the values. The resulting margins are then incorporated into the CAPP manufacturability algorithms, creating a design informed process planning analysis. © 2023 Soc. for the Advancement of Material and Process Engineering. All rights reserved.

关键词： Aircraft

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efficient Off-Policy algorithms for Structured Markov Decision Processes

Efficient Off-Policy Algorithms for Structured Markov Decisi...

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IEEE conference on Decision and Control

作者： Sourav Ganguly Raghuram Bharadwaj Diddigi Prabuchandran K J Department of Computer Science and Engineering Indian Institute of Technology Dharwad India International Institute of Information Technology Bangalore India

Reinforcement Learning (RL) algorithms help in training an autonomous agent to learn the optimal actions in an unknown environment. RL, in conjunction with neural networks, known as deep RL, has achieved remarkable success in many real-life practical applications. However, most of these algorithms are complex to train and require a lot of data to learn optimal decisions. Hence, it is imperative to develop RL algorithms that are simple and data efficient. In this work, we propose off-policy RL algorithms that can exploit special structures present in the optimal policy of the underlying Markov Decision process. The off-policy learning enables us to make use of the available data efficiently. To this end, we first propose an off-policy algorithm that estimates the value function of only those policies with the optimal policy structure to determine the best policy. We then propose two novel off-policy algorithms utilizing Upper Confidence Bound (UCB) with less time and space complexity than our first algorithm. Through extensive experimental evaluations on RL benchmark tasks, we illustrate the efficacy of the proposed algorithms.

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Into Summarization Techniques for IoT data Discovery Routing 14

Into Summarization Techniques for IoT Data Discovery Routing

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IEEE 14th International conference on Cloud Computing (CLOUD)

作者： Hieu Tran Son Nguyen Yen, I-Ling Bastani, Farokh Univ Texas Dallas Dept Comp Sci Richardson TX 75080 USA

ISBN: (纸本)9781665400602

In this paper, we consider the IoT data discovery problem in very large and growing scale networks. Specifically, we investigate in depth the routing table summarization techniques to support effective and space-efficient IoT data discovery routing. Novel summarization algorithms, including alphabetical based, hash based, and meaning based summarization and their corresponding coding schemes are proposed. The issue of potentially misleading routing due to summarization is also investigated. Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff between the routing table compression rate and the chance of causing misleading routing. For experimental study, we have collected 100K IoT data streams from various IoT databases as the input dataset. Experimental results show that our summarization solution can reduce the routing table size by 20 to 30 folds with 2-5% increase in latency when compared with similar peer-to-peer discovery routing algorithms without summarization. Also, our approach outperforms DHT based approaches by 2 to 6 folds in terms of latency and traffic.

关键词： IoT data Discovery Summarization Techniques Cloud Computing Distributing Systems

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A general framework for mining concept-drifting data streams with evolvable features 21

A general framework for mining concept-drifting data streams...

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21st IEEE International conference on data Mining (IEEE ICDM)

作者： Peng, Jiaqi Guo, Jinxia Yang, Qinli Lu, Jianyun Shao, Junmming Univ Elect Sci & Technol China Data Min Lab Chengdu Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China

ISBN: (纸本)9781665423984

Mining feature evolvable streams has gained increasing attention in recent years. However, most existing approaches are designed for stationary data streams (i.e., data streams without concept drifts) and often work with high time complexity due to the time-consuming optimization procedure. The two deficiencies thus largely limit its applications to real-world data stream scenarios. In this paper, we consider a more difficult but practical streaming setting: a data stream with both concept drifts and evolvable features. To this end, we propose a general framework for mining concept-drifting data streams with evolvable features, called FEMC, based on an efficient Feature Evolvable streaming learning and dynamic Micro-Clusters maintenance. Specifically, we derive a closed-form solution to preserve the information in vanished features by learning a weight vector on survival features. The evolving concepts, are further learnt by dynamically maintaining a set of micro-clusters with varying feature space on-the-fly. Empirical results on real-world data sets have demonstrated the benefits of the proposed framework on both clustering and classification tasks by comparing with state-of-the-art algorithms.

关键词： data stream mining Feature evolvable stream Concept drift

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