Suchergebnisse - theory of computation theory and algorithms for application domains machinery learning theory

Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications von Li, Xingguo

“… In modern “Big Data” applications, structured learning is the most widely employed methodology …”
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Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise von Wu, Jian, Sheng, Victor S, Zhang, Jing, Li, Hua, Dadakova, Tetiana, Swisher, Christine Leon, Cui, Zhiming, Zhao, Pengpeng

“… Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification …”
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Multi-Structural Games and Number of Quantifiers von Fagin, Ronald, Lenchner, Jonathan, Regan, Kenneth W., Vyas, Nikhil

“… to distinguish linear orders of different sizes, and develop machinery for analyzing structures beyond linear orders …”
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SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice von Ren, Rui, Yang, Jingbang, Yang, Linxiao, Gu, Xinyue, Sun, Liang

“… ) approach to select rules iteratively, maximizing a non-monotone submodular lower bound. Compared with existing fault localization algorithms, our algorithm can adapt …”
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Accelerating Decision-Tree-Based Inference Through Adaptive Parallelization von Van Lunteren, Jan

“… Gradient-boosted trees and random forests are among the most popular machine learning algorithms …”
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AutoDW: Automatic Data Wrangling Leveraging Large Language Models von Liu, Lei, Hasegawa, So, Sampat, Shailaja Keyur, Xenochristou, Maria, Chen, Wei-Peng, Kato, Takashi, Kakibuchi, Taisei, Asai, Tatsuya

“… Data wrangling is a critical yet often labor-intensive process, essential for transforming raw data into formats suitable for downstream tasks such as machine learning or data analysis …”
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Activation Sequence Caching: High-Throughput and Memory-Efficient Generative Inference with a Single GPU von Kim, Sowoong, Sim, Eunyeong, Shin, Youngsam, Cho, YeonGon, Baek, Woongki

“… To bridge this gap, this work presents an in-depth characterization study of KVC, which demonstrates that KVC makes a suboptimal trade-off between computations and communications …”
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Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection von Song, Yingjian, Pitafi, Zaid Farooq, Dou, Fei, Sun, Jin, Zhang, Xiang, Phillips, Bradley G, Song, Wenzhan

“… This paper introduces SeismoDot, which consists of a self-supervised learning module and a spectral-temporal feature fusion module for bed occupancy detection …”
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MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training von Bae, Jonghyun, Choi, Jong Youl, Pasini, Massimiliano Lupo, Mehta, Kshitij, Zhang, Pei, Ibrahim, Khaled Z.

“… Scalable data management is essential for processing large scientific dataset on HPC platforms for distributed deep learning …”
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ALISE: Accelerating Large Language Model Serving with Speculative Scheduling von Zhao, Youpeng, Wang, Jun

“… ). As exemplified by ChatGPT, LLM-based applications necessitate minimal response latency and maximal throughput for inference serving …”
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Bayesian synthesis of probabilistic programs for automatic data modeling von Saad, Feras A., Cusumano-Towner, Marco F., Schaechtle, Ulrich, Rinard, Martin C., Mansinghka, Vikash K.

“… We also derive a general class of synthesis algorithms for domain-specific languages specified by probabilistic context-free grammars and establish the soundness of our approach for these languages …”
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Local Higher-Order Graph Clustering von Yin, Hao, Benson, Austin R, Leskovec, Jure, Gleich, David F

“… Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable …”
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Distinguishing Hidden Markov Chains von Kiefer, Stefan, Sistla, A. Prasad

“… Motivated by applications in stochastic runtime verification, we consider the problem of distinguishing two given HMCs based on a single observation sequence that one of the HMCs generates …”
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Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods von Poulain, Raphael, Tarek, Mirza Farhan Bin, Beheshti, Rahmatollah

“… Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare …”
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Linear Scheduling of Big Data Streams on Multiprocessor Sets in the Cloud von Tantalaki, Nicoleta, Souravlas, Stavros, Roumeliotis, Manos, Katsavounis, Stefanos

“… (dynamically or not) and route streaming data between them. Efficient scheduling of processing tasks of data flows can reduce application latencies and eliminate network congestion …”
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A Memory-Efficient Markov Decision Process Computation Framework Using BDD-based Sampling Representation von Zhou, He, Hu, Jiang, Khatri, Sunil P., Liu, Frank

“… * Theory of computation \rightarrow Data compression; Parallel algorithms; Sequential decision making; * Mathematics of computing \rightarrowBayesian computation; …”
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RL-Fill: Timing-Aware Fill Insertion Using Reinforcement Learning von Cho, Jinoh, Park, Seonghyeon, Lee, Jakang, Lee, Sung-Yun, Ahn, Jinmo, Kang, Seokhyeong

“… We introduce RL-Fill, a novel reinforcement learning framework for timing-aware fill insertion …”
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Robust Implementation of Retrieval-Augmented Generation on Edge-Based Computing-in-Memory Architectures von Qin, Ruiyang, Yan, Zheyu, Zeng, Dewen, Jia, Zhenge, Liu, Dancheng, Liu, Jianbo, Abbasi, Ahmed, Zheng, Zhi, Cao, Ningyuan, Ni, Kai, Xiong, Jinjun, Shi, Yiyu

“… Although such learning methods can be optimized to reduce resource utilization, the overall required resources remain a heavy burden on edge devices …”
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Laminar 2.0: Serverless Stream Processing with Enhanced Code Search and Recommendations von Rotchford, Daniel, Evans, Samuel, Filgueira, Rosa

“… Building on Laminar 1.0, this version introduces improved dependency management, client-server functionality, and advanced deep learning models for semantic search …”
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Using Static Analysis to Aid Monolith to Microservice System Transformation: Tuning Fuzzy c-Means in a VAE-Based GNN Approach von Sooksatra, Korn, Hossain Chy, Md Showkat, Arju, Md Ashfakur Rahman, Cerny, Tomas, Rivas, Pablo

“… This paper explores a machine learning-driven approach to decompose monolithic systems into microservices, targeting maintainability and modularization …”
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