:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteur principal:	Shamim, Ryan
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2601.00847
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
par: Han, Xiaotian, et autres
Publié: (2025)

Adaptive Test-Time Training for Predicting Need for Invasive Mechanical Ventilation in Multi-Center Cohorts
par: Lu, Xiaolei, et autres
Publié: (2025)

Attention Once Is All You Need: Efficient Streaming Inference with Stateful Transformers
par: Norgren, Victor
Publié: (2026)

The Residual Stream Is All You Need: On the Redundancy of the KV Cache in Transformer Inference
par: Qasim, Kaleem Ullah, et autres
Publié: (2026)

Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
par: Tyukin, Georgy, et autres
Publié: (2024)

Context is All You Need
par: Delanois, Jean Erik, et autres
Publié: (2026)

Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling
par: Shao, Shitong, et autres
Publié: (2023)

Probabilities Are All You Need: A Probability-Only Approach to Uncertainty Estimation in Large Language Models
par: Nguyen, Manh, et autres
Publié: (2025)

You Only Accept Samples Once: Fast, Self-Correcting Stochastic Variational Inference
par: Dayta, Dominic B.
Publié: (2024)

You Only Need Minimal RLVR Training: Extrapolating LLMs via Rank-1 Trajectories
par: Wei, Zhepei, et autres
Publié: (2026)

Occam's Razor is Only as Sharp as Your ELBO
par: Harvey, Ethan, et autres
Publié: (2026)

SmartMem: Layout Transformation Elimination and Adaptation for Efficient DNN Execution on Mobile
par: Niu, Wei, et autres
Publié: (2024)

You Only Train Once
par: Sakaridis, Christos
Publié: (2025)

Scalable Data Attribution via Forward-Only Test-Time Inference
par: Ma, Sibo, et autres
Publié: (2025)

Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need
par: Peng, Sijia, et autres
Publié: (2024)

Attention is All You Need Until You Need Retention
par: Yaslioglu, M. Murat
Publié: (2025)

FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning
par: Li, Zitong, et autres
Publié: (2025)

Harnesses for Inference-Time Alignment over Execution Trajectories
par: Wang, Boyuan, et autres
Publié: (2026)

Accuracy is Not All You Need
par: Dutta, Abhinav, et autres
Publié: (2024)

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
par: Hussain, Md Shamim, et autres
Publié: (2024)

Attention Is Not All You Need: The Importance of Feedforward Networks in Transformer Models
par: Gerber, Isaac
Publié: (2025)

Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?
par: Cen, Jiacheng, et autres
Publié: (2024)

Masked Generative Transformer Is What You Need for Image Editing
par: Chow, Wei, et autres
Publié: (2026)

Embedding Is (Almost) All You Need: Retrieval-Augmented Inference for Generalizable Genomic Prediction Tasks
par: Datta, Nirjhor, et autres
Publié: (2025)

Improving Prediction of Need for Mechanical Ventilation using Cross-Attention
par: Mohanty, Anwesh, et autres
Publié: (2024)

Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking
par: LeVine, Will, et autres
Publié: (2025)

You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
par: P, Srivatsa, et autres
Publié: (2020)

Optimisation Is Not What You Need
par: Ibias, Alfredo
Publié: (2025)

Simple Feedfoward Neural Networks are Almost All You Need for Time Series Forecasting
par: Sun, Fan-Keng, et autres
Publié: (2025)

Grokking as Structural Inference: Transformers Need Bayesian Lottery Tickets
par: Hidajat, Kai, et autres
Publié: (2026)

Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
par: Chen, Junfeng, et autres
Publié: (2024)

Some Attention is All You Need for Retrieval
par: Michalak, Felix, et autres
Publié: (2025)

Half Search Space is All You Need
par: Rumiantsev, Pavel, et autres
Publié: (2025)

Top-$nσ$: Not All Logits Are You Need
par: Tang, Chenxia, et autres
Publié: (2024)

Deterministic Inference across Tensor Parallel Sizes That Eliminates Training-Inference Mismatch
par: Zhang, Ziyang, et autres
Publié: (2025)

Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
par: Wolters, Christopher, et autres
Publié: (2024)

You Only Train Once: Differentiable Subset Selection for Omics Data
par: Chopard, Daphné, et autres
Publié: (2025)

Exploitation Is All You Need... for Exploration
par: Rentschler, Micah, et autres
Publié: (2025)

Reinforcement Learning is all You Need
par: Lian, Yongsheng
Publié: (2025)

You Need Better Attention Priors
par: Litman, Elon, et autres
Publié: (2026)