Výsledky vyhľadávania - theory of computation theory and algorithms for application domains machinery learning theory

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

“…In modern “Big Data” applications, structured learning is the most widely employed methodology…”
Získať plný text

Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise Autor 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…”
Zistit podrobnosti o prístupe

Multi-Structural Games and Number of Quantifiers Autor 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…”
Získať plný text

SLIM: a Scalable and Interpretable Light-weight Fault Localization Algorithm for Imbalanced Data in Microservice Autor 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…”
Získať plný text

Accelerating Decision-Tree-Based Inference Through Adaptive Parallelization Autor Van Lunteren, Jan

“…Gradient-boosted trees and random forests are among the most popular machine learning algorithms…”
Získať plný text

AutoDW: Automatic Data Wrangling Leveraging Large Language Models Autor 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…”
Získať plný text

Activation Sequence Caching: High-Throughput and Memory-Efficient Generative Inference with a Single GPU Autor 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…”
Získať plný text

Self-Supervised Representation Learning and Temporal-Spectral Feature Fusion for Bed Occupancy Detection Autor 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…”
Zistit podrobnosti o prístupe

MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training Autor 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…”
Získať plný text

ALISE: Accelerating Large Language Model Serving with Speculative Scheduling Autor Zhao, Youpeng, Wang, Jun

“…). As exemplified by ChatGPT, LLM-based applications necessitate minimal response latency and maximal throughput for inference serving…”
Získať plný text

Bayesian synthesis of probabilistic programs for automatic data modeling Autor 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…”
Získať plný text

Local Higher-Order Graph Clustering Autor 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…”
Zistit podrobnosti o prístupe

Distinguishing Hidden Markov Chains Autor 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…”
Získať plný text

Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods Autor 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…”
Zistit podrobnosti o prístupe

Linear Scheduling of Big Data Streams on Multiprocessor Sets in the Cloud Autor 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…”
Získať plný text

A Memory-Efficient Markov Decision Process Computation Framework Using BDD-based Sampling Representation Autor 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;…”
Získať plný text

RL-Fill: Timing-Aware Fill Insertion Using Reinforcement Learning Autor 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…”
Získať plný text

Robust Implementation of Retrieval-Augmented Generation on Edge-Based Computing-in-Memory Architectures Autor 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…”
Získať plný text

Laminar 2.0: Serverless Stream Processing with Enhanced Code Search and Recommendations Autor 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…”
Získať plný text

Using Static Analysis to Aid Monolith to Microservice System Transformation: Tuning Fuzzy c-Means in a VAE-Based GNN Approach Autor 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…”
Získať plný text