Image de couverture de livre

Dimension Mixer: Group Mixing of Input Dimensions for Efficient Function Approximation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Sapkota, Suman, Bhattarai, Binod
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Input Invex Neural Network
par: Sapkota, Suman, et autres
Publié: (2021)

Metric as Transform: Exploring beyond Affine Transform for Interpretable Neural Network
par: Sapkota, Suman
Publié: (2024)

General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
par: Lai, Kuo-Wei, et autres
Publié: (2023)

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions
par: Lu, Yulong, et autres
Publié: (2025)

Beyond Token Eviction: Mixed-Dimension Budget Allocation for Efficient KV Cache Compression
par: Miao, Ruijie, et autres
Publié: (2026)

MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer
par: Mitha, Samir, et autres
Publié: (2023)

Variational LSTM with Augmented Inputs: Nonlinear Response History Metamodeling with Aleatoric and Epistemic Uncertainty
par: Sapkota, Manisha, et autres
Publié: (2026)

Dimension-Free Decision Calibration for Nonlinear Loss Functions
par: Tang, Jingwu, et autres
Publié: (2025)

NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance
par: Chhetri, Anju, et autres
Publié: (2025)

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
par: Khanal, Bidur, et autres
Publié: (2024)

Anchoring Values in Temporal and Group Dimensions for Flow Matching Model Alignment
par: Shao, Yawen, et autres
Publié: (2025)

Hardware-Friendly Input Expansion for Accelerating Function Approximation
par: Lou, Hu, et autres
Publié: (2026)

The No-Clash Teaching Dimension is Bounded by VC Dimension
par: Liu, Jiahua, et autres
Publié: (2026)

Learning Functional Graphs with Nonlinear Sufficient Dimension Reduction
par: Kim, Kyongwon, et autres
Publié: (2026)

Low-Dimension-to-High-Dimension Generalization And Its Implications for Length Generalization
par: Chen, Yang, et autres
Publié: (2024)

Rescaled Influence Functions: Accurate Data Attribution in High Dimension
par: Rubinstein, Ittai, et autres
Publié: (2025)

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
par: Schmidt, Jonathan, et autres
Publié: (2023)

Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
par: Zhang, Jun-Jie, et autres
Publié: (2024)

Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures
par: Li, Gen, et autres
Publié: (2025)

TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
par: Wang, Shiyu, et autres
Publié: (2024)

LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
par: Kowsher, Md, et autres
Publié: (2024)

FFNet: MetaMixer-based Efficient Convolutional Mixer Design
par: Yun, Seokju, et autres
Publié: (2024)

How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
par: Khanal, Bidur, et autres
Publié: (2024)

Approximating Uniform Random Rotations by Two-Block Structured Hadamard Rotations in High Dimensions
par: Zilca, Tomer, et autres
Publié: (2026)

DimGrow: Memory-Efficient Field-level Embedding Dimension Search
par: Huang, Yihong, et autres
Publié: (2025)

Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
par: Joarder, Shourov, et autres
Publié: (2026)

The Dimension of Self-Directed Learning
par: Devulapalli, Pramith, et autres
Publié: (2024)

Dimension Agnostic Neural Processes
par: Lee, Hyungi, et autres
Publié: (2025)

Dimension Reduction for Symbolic Regression
par: Kahlmeyer, Paul, et autres
Publié: (2025)

Active Subspaces in Infinite Dimension
par: Kundu, Poorbita, et autres
Publié: (2025)

PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting
par: Gong, Zeying, et autres
Publié: (2023)

Multimodal Federated Learning With Missing Modalities through Feature Imputation Network
par: Poudel, Pranav, et autres
Publié: (2025)

Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
par: Takakura, Shokichi, et autres
Publié: (2023)

U-Mixer: An Unet-Mixer Architecture with Stationarity Correction for Time Series Forecasting
par: Ma, Xiang, et autres
Publié: (2024)

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
par: Zhang, Xu, et autres
Publié: (2026)

MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
par: Chergui, Abdelmadjid, et autres
Publié: (2024)

On the Intrinsic Dimensions of Data in Kernel Learning
par: Takhanov, Rustem
Publié: (2026)

Exploring the Dimensions of a Variational Neuron
par: Ruffenach, Yves
Publié: (2026)

Structural Dimension Reduction in Bayesian Networks
par: Heng, Pei, et autres
Publié: (2026)

Dimension Reduction with Locally Adjusted Graphs
par: Wang, Yingfan, et autres
Publié: (2024)