Copertina

Addition is almost all you need: Compressing large language models with double binary factorization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Boža, Vladimír, Macko, Vladimír
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization
di: Boža, Vladimír, et al.
Pubblicazione: (2024)

MACKO: Sparse Matrix-Vector Multiplication for Low Sparsity
di: Macko, Vladimír, et al.
Pubblicazione: (2025)

Attention and Compression is all you need for Controllably Efficient Language Models
di: Prakash, Jatin, et al.
Pubblicazione: (2025)

Fast and Effective Weight Update for Pruned Large Language Models
di: Boža, Vladimír
Pubblicazione: (2024)

One protein is all you need
di: Bushuiev, Anton, et al.
Pubblicazione: (2024)

Kolmogorov GAM Networks are all you need!
di: Polson, Sarah, et al.
Pubblicazione: (2025)

KV-weights are all you need for skipless transformers
di: Graef, Nils
Pubblicazione: (2024)

Tabular Data: Is Deep Learning all you need?
di: Zabërgja, Guri, et al.
Pubblicazione: (2024)

Image compositing is all you need for data augmentation
di: Shermaine, Ang Jia Ning, et al.
Pubblicazione: (2025)

Large Language Models aren't all that you need
di: Holla, Kiran Voderhobli, et al.
Pubblicazione: (2024)

Experts are all you need: A Composable Framework for Large Language Model Inference
di: Sridharan, Shrihari, et al.
Pubblicazione: (2025)

Graph is all you need? Lightweight data-agnostic neural architecture search without training
di: Huang, Zhenhan, et al.
Pubblicazione: (2024)

Cross-Modal Safety Alignment: Is textual unlearning all you need?
di: Chakraborty, Trishna, et al.
Pubblicazione: (2024)

Attention is all you need for boosting graph convolutional neural network
di: Wu, Yinwei
Pubblicazione: (2024)

Linear attention is (maybe) all you need (to understand transformer optimization)
di: Ahn, Kwangjun, et al.
Pubblicazione: (2023)

Simulation-based inference with scattering representations: scattering is all you need
di: Lin, Kiyam, et al.
Pubblicazione: (2024)

DC is all you need: describing ReLU from a signal processing standpoint
di: Kechris, Christodoulos, et al.
Pubblicazione: (2024)

Few Labels are all you need: A Weakly Supervised Framework for Appliance Localization in Smart-Meter Series
di: Petralia, Adrien, et al.
Pubblicazione: (2025)

Neural Operator: Is data all you need to model the world? An insight into the paradigm of data-driven scientific ML
di: Viswanath, Hrishikesh, et al.
Pubblicazione: (2023)

Attention is all you need for an improved CNN-based flash flood susceptibility modeling. The case of the ungauged Rheraya watershed, Morocco
di: Elghouat, Akram, et al.
Pubblicazione: (2024)

Slim attention: cut your context memory in half without loss -- K-cache is all you need for MHA
di: Graef, Nils, et al.
Pubblicazione: (2025)

Anti-concentration is (almost) all you need
di: Heinrich, Markus, et al.
Pubblicazione: (2025)

Is attention all you need in medical image analysis? A review
di: Papanastasiou, Giorgos, et al.
Pubblicazione: (2023)

Block removal for large language models through constrained binary optimization
di: Jansen, David, et al.
Pubblicazione: (2026)

1 bit is all we need: binary normalized neural networks
di: Cabral, Eduardo Lobo Lustoda, et al.
Pubblicazione: (2025)

Why you don't overfit, and don't need Bayes if you only train for one epoch
di: Aitchison, Laurence
Pubblicazione: (2024)

Signformer is all you need: Towards Edge AI for Sign Language
di: Yang, Eta
Pubblicazione: (2024)

Visual cognition in multimodal large language models
di: Buschoff, Luca M. Schulze, et al.
Pubblicazione: (2023)

Hypothesis generation and updating in large language models
di: Xiong, Hua-Dong
Pubblicazione: (2026)

Representation in large language models
di: Yetman, Cameron
Pubblicazione: (2025)

Compressed models are NOT miniature versions of large models
di: Rai, Rohit Raj, et al.
Pubblicazione: (2024)

A Bayesian Optimization approach for calibrating large-scale activity-based transport models
di: Agriesti, Serio, et al.
Pubblicazione: (2023)

100 instances is all you need: predicting the success of a new LLM on unseen data by testing on a few instances
di: Pacchiardi, Lorenzo, et al.
Pubblicazione: (2024)

Amortizing intractable inference in large language models
di: Hu, Edward J., et al.
Pubblicazione: (2023)

Layer-wise dynamic rank for compressing large language models
di: Mi, Zhendong, et al.
Pubblicazione: (2025)

NIRVANA: Structured pruning reimagined for large language models compression
di: Ai, Mengting, et al.
Pubblicazione: (2025)

Training microrobots to swim by a large language model
di: Xu, Zhuoqun, et al.
Pubblicazione: (2024)

Alignment faking in large language models
di: Greenblatt, Ryan, et al.
Pubblicazione: (2024)

AI-AI Bias: large language models favor communications generated by large language models
di: Laurito, Walter, et al.
Pubblicazione: (2024)

Residual vector quantization for KV cache compression in large language model
di: Kumar, Ankur
Pubblicazione: (2024)