Results 301 to 310 of about 2,400,349 (361)
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SIAM Journal on Discrete Mathematics, 1995
Summary: A vertex (edge) coloring \(\phi:V\rightarrow \{1,2,\ldots,t\}\) (\(\phi':E\rightarrow \{1,2,\ldots, t\})\) of a graph \(G=(V,E)\) is a vertex (edge) \(t\)-ranking if, for any two vertices (edges) of the same color, every path between them contains a vertex (edge) of larger color. The vertex ranking number \(\chi_{r}(G)\) (edge ranking number \(
Hans L. Bodlaender +6 more
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Summary: A vertex (edge) coloring \(\phi:V\rightarrow \{1,2,\ldots,t\}\) (\(\phi':E\rightarrow \{1,2,\ldots, t\})\) of a graph \(G=(V,E)\) is a vertex (edge) \(t\)-ranking if, for any two vertices (edges) of the same color, every path between them contains a vertex (edge) of larger color. The vertex ranking number \(\chi_{r}(G)\) (edge ranking number \(
Hans L. Bodlaender +6 more
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The Expressive Power of Low-Rank Adaptation
International Conference on Learning Representations, 2023Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method that leverages low-rank adaptation of weight matrices, has emerged as a prevalent technique for fine-tuning pre-trained models such as large language models and diffusion models ...
Yuchen Zeng, Kangwook Lee
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Ranking and adaptive ranking CDMA
IEEE/ACM Transactions on Networking, 2005This paper develops an uplink transmission scheduling scheme, referred to as Ranking CDMA, which selects a subset of users for transmission at each time slot. We introduce long- and short-term metrics that characterize its performance, and devise analytical methods for evaluating these performance measures.
Pierre T. Kabamba +2 more
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DoRA: Weight-Decomposed Low-Rank Adaptation
International Conference on Machine LearningAmong the widely used parameter-efficient fine-tuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs.
Shih-Yang Liu +6 more
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Rankings and Ranking Functions
Canadian Journal of Mathematics, 1981Suppose that n competitors compete in r races and in each race they are awarded placings l, 2, 3, …, n – 1, n. After the r races each competitor has a result consisting of his r placings. Let such a result be written (αj)1≦j≦r where for convenience the positive integers αj are arranged in ascending order.
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GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
International Conference on Machine LearningTraining Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix ...
Jiawei Zhao +5 more
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LoRA+: Efficient Low Rank Adaptation of Large Models
International Conference on Machine LearningIn this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021) leads to suboptimal finetuning of models with large width (embedding dimension). This is due to the fact that adapter matrices A and B in LoRA are updated
Soufiane Hayou, Nikhil Ghosh, Bin Yu
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rle, 2013 Abstract Transitivity is a fundamental requirement for consistency. Legal systems, especially when composed over time and by different agencies, may encounter non-transitive cycles, in which by one rule the law prefers one outcome a over another outcome b
Barak Medina, Shlomo Naeh, Uzi Segal
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Proceeding Structure in Complexity Theory, 1987
This paper structurally characterizes the complexity of ranking. A set is P-rankable if there is a polynomial time computable function $f$ so that for all $x, f(x)$ computes the number of elements of $A$ that are lexicographically $\leq x$, i.e., the rank of $x$ with respect to $A$. We'll say a class $C$ is P-rankable if all sets in $C$ are P-rankable.
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This paper structurally characterizes the complexity of ranking. A set is P-rankable if there is a polynomial time computable function $f$ so that for all $x, f(x)$ computes the number of elements of $A$ that are lexicographically $\leq x$, i.e., the rank of $x$ with respect to $A$. We'll say a class $C$ is P-rankable if all sets in $C$ are P-rankable.
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SIAM Journal on Computing, 1985
A notion of language compressibility is defined and it is proved that in a sufficiently sparse and ``easy''-computable language essentially all strings can be compressed efficiently. Similar results hold for a type of optimal compression (ranking). Examples of languages that cannot be compressed/ranked efficiently are also presented, as well as some ...
Andrew V. Goldberg, Michael Sipser
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A notion of language compressibility is defined and it is proved that in a sufficiently sparse and ``easy''-computable language essentially all strings can be compressed efficiently. Similar results hold for a type of optimal compression (ranking). Examples of languages that cannot be compressed/ranked efficiently are also presented, as well as some ...
Andrew V. Goldberg, Michael Sipser
openaire +2 more sources