Risultati della ricerca - decoding (algorithmics OR (algorithm OR (algorithm OR algorithm)))

  1. 1681

    The weight hierarchy of decreasing norm-trace codes di Camps-Moreno, Eduardo, López, Hiram H., Matthews, Gretchen L., San-José, Rodrigo

    Pubblicazione 2024
    Sommario: “... of type II, $t$-resilient functions, bounding the cardinality of the output in list decoding algorithms...”
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  2. 1682
  3. 1683

    Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning di Park, Sungjin, Liu, Xiao, Gong, Yeyun, Choi, Edward

    Pubblicazione 2024
    Sommario: “... reasoning benchmarks demonstrate that our approach outperforms both single language model decoding...”
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  4. 1684

    HashEvict: A Pre-Attention KV Cache Eviction Strategy using Locality-Sensitive Hashing di Liu, Minghui, Rabbani, Tahseen, O'Halloran, Tony, Sankaralingam, Ananth, Hartley, Mary-Anne, Huang, Furong, Fermüller, Cornelia, Aloimonos, Yiannis

    Pubblicazione 2024
    Sommario: “... significant GPU memory. In this work, we introduce HashEvict, an algorithm that uses locality-sensitive...”
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  5. 1685

    Shifting-Merging: Secure, High-Capacity and Efficient Steganography via Large Language Models di Bai, Minhao, Yang, Jinshuai, Pang, Kaiyi, Huang, Yongfeng, Gao, Yue

    Pubblicazione 2025
    Sommario: “... of ShiMer, we design a reordering algorithm to minimize the occurrence of interval splitting during decoding...”
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  6. 1686
  7. 1687

    Masked Generative Nested Transformers with Decode Time Scaling di Goyal, Sahil, Tula, Debapriya, Jain, Gagan, Shenoy, Pradeep, Jain, Prateek, Paul, Sujoy

    Pubblicazione 2025
    Sommario: “... computational efficiency. Most of these algorithms involve multiple passes over a transformer model to generate...”
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  8. 1688

    IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information di Chen, Shenghui, Zhao, Ruihan, Chinchali, Sandeep, Topcu, Ufuk

    Pubblicazione 2025
    Sommario: “... Information Gain Monte Carlo Tree Search (IG-MCTS), an online planning algorithm that jointly optimizes...”
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  9. 1689

    Uplink OFDM Channel Prediction with Hybrid CNN-LSTM for 6G Non-Terrestrial Networks di De Filippo, Bruno, Amatetti, Carla, Vanelli-Coralli, Alessandro

    Pubblicazione 2025
    Sommario: “... response (CFR) matrix in absence of pilots. The algorithm employs an encoder-decoder convolutional neural...”
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  10. 1690

    Exploring Contextual Attribute Density in Referring Expression Counting di Wang, Zhicheng, Pan, Zhiyu, Peng, Zhan, Cheng, Jian, Xiao, Liwen, Jiang, Wei, Cao, Zhiguo

    Pubblicazione 2025
    Sommario: “...Referring expression counting (REC) algorithms are for more flexible and interactive counting...”
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  11. 1691

    SINR: Sparsity Driven Compressed Implicit Neural Representations di Jayasundara, Dhananjaya, Rajagopalan, Sudarshan, Ranasinghe, Yasiru, Tran, Trac D., Patel, Vishal M.

    Pubblicazione 2025
    Sommario: “... coding schemes employed. In this paper, we introduce SINR, an innovative compression algorithm...”
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  12. 1692

    Decoding the mechanisms of the Hattrick football manager game using Bayesian network structure learning di Constantinou, Anthony C., Higgins, Nicholas, Kitson, Neville K.

    Pubblicazione 2025
    Sommario: “.... Hattrick's game-engine mechanics are partially hidden, and users have attempted to decode them...”
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  13. 1693

    Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models di Liu, Yi, Liu, Dianqing, Zhu, Mingye, Guo, Junbo, Zhang, Yongdong, Mao, Zhendong

    Pubblicazione 2025
    Sommario: “... algorithm with iterative token-level decoding to address the common first-token latency issue in comparable...”
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  14. 1694

    Mamba Drafters for Speculative Decoding di Choi, Daewon, Oh, Seunghyuk, Dingliwal, Saket, Tack, Jihoon, Kim, Kyuyoung, Song, Woomin, Kim, Seojin, Han, Insu, Shin, Jinwoo, Galstyan, Aram, Katiyar, Shubham, Bodapati, Sravan Babu

    Pubblicazione 2025
    Sommario: “...Speculative decoding has emerged as a promising approach to accelerating large language model (LLM...”
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  15. 1695

    Leveraging erasure errors in logical qubits with metastable $^{171}$Yb atoms di Zhang, Bichen, Liu, Genyue, Bornet, Guillaume, Horvath, Sebastian P., Peng, Pai, Ma, Shuo, Huang, Shilin, Puri, Shruti, Thompson, Jeff D.

    Pubblicazione 2025
    Sommario: “...Implementing large-scale quantum algorithms with practical advantage will require fault-tolerance...”
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  16. 1696

    $\texttt{SPECS}$: Faster Test-Time Scaling through Speculative Drafts di Cemri, Mert, Rajaraman, Nived, Tiwari, Rishabh, Liu, Xiaoxuan, Keutzer, Kurt, Stoica, Ion, Ramchandran, Kannan, Beirami, Ahmad, Sun, Ziteng

    Pubblicazione 2025
    Sommario: “...{SPECS}$, a latency-aware test-time scaling method inspired by speculative decoding. $\texttt{SPECS...”
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  17. 1697

    INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling di Dong, Xin, Dong, Shichao, Wang, Jin, Huang, Jing, Zhou, Li, Sun, Zenghui, Jing, Lihua, Lan, Jingsong, Zhu, Xiaoyong, Zheng, Bo

    Pubblicazione 2025
    Sommario: “... further propose \textbf{INTER}: \textbf{Inter}action Guidance Sampling, a novel training-free algorithm...”
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  18. 1698

    Teaching Transformers to Solve Combinatorial Problems through Efficient Trial & Error di Giannoulis, Panagiotis, Pantis, Yorgos, Tzamos, Christos

    Pubblicazione 2025
    Sommario: “.... Unlike prior work that used custom architectures, our method employs a vanilla decoder-only Transformer...”
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  19. 1699

    AMLA: MUL by ADD in FlashAttention Rescaling di Liao, Qichen, Hu, Chengqiu, Miao, Fangzheng, Li, Bao, Liu, Yiyang, Lyu, Junlong, Jiang, Lirui, Wang, Jun, Zheng, Lingchao, Li, Jun, Fan, Yuwei

    Pubblicazione 2025
    Sommario: “.... This poses challenges for efficient hardware implementation -- especially during the decode phase. This paper...”
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  20. 1700

    The dimension and Bose distance of some BCH codes of length $\frac{q^{m}-1}λ$ di Zheng, Run, Sze, Nung-Sing, Huang, Zejun

    Pubblicazione 2025
    Sommario: “... structure, multi-error correcting capability, and efficient decoding algorithms. Despite their practical...”
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