Search Results - theory of computation theory and algorithms for applications domains machinery learning theory

Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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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MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning by Dymchenko, Sofya, Purandare, Abhishek, Raffin, Bruno

“… In this paper we introduce a new active learning method to enhance data-efficiency for on-line surrogate training…”
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