Graph is all you need? Lightweight data-agnostic neural architecture search without training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Zhenhan, Pedapati, Tejaswini, Chen, Pin-Yu, Jiang, Chunheng, Gao, Jianxi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Intermediate Representations are Strong AI-Generated Image Detectors
by: Huang, Zhenhan, et al.
Published: (2026)

Differentiable Prompt Learning for Vision Language Models
by: Huang, Zhenhan, et al.
Published: (2024)

Modular Prompt Learning Improves Vision-Language Models
by: Huang, Zhenhan, et al.
Published: (2025)

From PEFT to DEFT: Parameter Efficient Finetuning for Reducing Activation Density in Transformers
by: Runwal, Bharat, et al.
Published: (2024)

Sparse Gradient Compression for Fine-Tuning Large Language Models
by: Yang, David H., et al.
Published: (2025)

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

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

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

OjaKV: Context-Aware Online Low-Rank KV Cache Compression
by: Zhu, Yuxuan, et al.
Published: (2025)

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

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

TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
by: Khatiwada, Aamod, et al.
Published: (2024)

Additive regularization schedule for neural architecture search
by: Potanin, Mark, et al.
Published: (2024)

STAR: Spectral Truncation and Rescale for Model Merging
by: Lee, Yu-Ang, et al.
Published: (2025)

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

Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator
by: Soin, Hartej, et al.
Published: (2024)

ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval
by: Yang, David H., et al.
Published: (2026)

Unraveling the cognitive patterns of Large Language Models through module communities
by: Bhandari, Kushal Raj, et al.
Published: (2025)

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

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

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

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

Large Language Model Confidence Estimation via Black-Box Access
by: Pedapati, Tejaswini, et al.
Published: (2024)

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

Global optimization of graph acquisition functions for neural architecture search
by: Xie, Yilin, et al.
Published: (2025)

Task agnostic continual learning with Pairwise layer architecture
by: Keskinen, Santtu
Published: (2024)

Large Language Models can be Strong Self-Detoxifiers
by: Ko, Ching-Yun, et al.
Published: (2024)

Predicting Time Series of Networked Dynamical Systems without Knowing Topology
by: Ding, Yanna, et al.
Published: (2024)

Addition is almost all you need: Compressing large language models with double binary factorization
by: Boža, Vladimír, et al.
Published: (2025)

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

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

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

Chain-structured neural architecture search for financial time series forecasting
by: Levchenko, Denis, et al.
Published: (2024)

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

CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions
by: Puri, Isha, et al.
Published: (2025)

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

A framework for measuring the training efficiency of a neural architecture
by: Cueto-Mendoza, Eduardo, et al.
Published: (2024)

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

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

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models
by: Dhurandhar, Amit, et al.
Published: (2024)