:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Guoqiang, Heusdens, Richard
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2409.04275
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Distributed Nonlinear Conic Optimisation with partially separable Structure
by: Heusdens, Richard, et al.
Published: (2024)

Distributed Optimisation with Linear Equality and Inequality Constraints using PDMM
by: Heusdens, Richard, et al.
Published: (2023)

Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization
by: Yu, Wenrui, et al.
Published: (2024)

First Attentions Last: Better Exploiting First Attentions for Efficient Transformer Training
by: Kim, Gyudong, et al.
Published: (2025)

Streaming Attention Approximation via Discrepancy Theory
by: Kochetkova, Ekaterina, et al.
Published: (2025)

Optimizing Attention on GPUs by Exploiting GPU Architectural NUMA Effects
by: Choudhary, Mansi, et al.
Published: (2025)

The Structural Origin of Attention Sink: Variance Discrepancy, Super Neurons, and Dimension Disparity
by: Li, Siquan, et al.
Published: (2026)

Attention Needs to Focus: A Unified Perspective on Attention Allocation
by: Fu, Zichuan, et al.
Published: (2026)

A Discrepancy-Based Perspective on Dataset Condensation
by: Chen, Tong, et al.
Published: (2025)

Graph Triple Attention Network: A Decoupled Perspective
by: Wang, Xiaotang, et al.
Published: (2024)

Flex Attention: A Programming Model for Generating Optimized Attention Kernels
by: Dong, Juechu, et al.
Published: (2024)

On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective
by: Mongaras, Gabriel, et al.
Published: (2025)

Few-Shot Design Optimization by Exploiting Auxiliary Information
by: Mani, Arjun, et al.
Published: (2026)

Optimal Privacy-Preserving Distributed Median Consensus
by: Yu, Wenrui, et al.
Published: (2025)

On the Optimization Landscape of Maximum Mean Discrepancy
by: Alon, Itai, et al.
Published: (2021)

Optimizing Mixture of Block Attention
by: Xiao, Guangxuan, et al.
Published: (2025)

The Key to State Reduction in Linear Attention: A Rank-based Perspective
by: Nazari, Philipp, et al.
Published: (2026)

AnchorAttention: Difference-Aware Sparse Attention with Stripe Granularity
by: Zhang, Yu, et al.
Published: (2025)

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences
by: Sun, Ao, et al.
Published: (2024)

When and Why Grouping Attention Heads Accelerates Muon Optimization
by: Zhang, Hongtao, et al.
Published: (2026)

Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
by: Bhattacharjee, Robi, et al.
Published: (2024)

Attention Sinks and Outliers in Attention Residuals
by: Luo, Haozheng, et al.
Published: (2026)

Attention Meets Post-hoc Interpretability: A Mathematical Perspective
by: Lopardo, Gianluigi, et al.
Published: (2024)

Attention as Binding: A Vector-Symbolic Perspective on Transformer Reasoning
by: Dhayalkar, Sahil Rajesh
Published: (2025)

AP-OOD: Attention Pooling for Out-of-Distribution Detection
by: Hofmann, Claus, et al.
Published: (2026)

Adversarial Lens: Exploiting Attention Layers to Generate Adversarial Examples for Evaluation
by: Dhole, Kaustubh
Published: (2025)

Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self-Attention: A Mixture-of-Experts Perspective
by: Yan, Fanqi, et al.
Published: (2025)

On the Optimization and Generalization of Multi-head Attention
by: Deora, Puneesh, et al.
Published: (2023)

Orthogonal Self-Attention
by: Zhang, Leo, et al.
Published: (2026)

Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss
by: Yu, Wenrui, et al.
Published: (2024)

Unveiling and Controlling Anomalous Attention Distribution in Transformers
by: Yan, Ruiqing, et al.
Published: (2024)

SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
by: Zhang, Jintao, et al.
Published: (2024)

Self-Attention as Distributional Projection: A Unified Interpretation of Transformer Architecture
by: Mehta, Nihal
Published: (2025)

Beyond Classical Attention: Quantum Attention for Scalable Computation
by: Guo, Xuyang, et al.
Published: (2023)

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
by: Mitsuzawa, Kensuke, et al.
Published: (2023)

Transformers for Tabular Data: A Training Perspective of Self-Attention via Optimal Transport
by: Quadrio, Alessandro, et al.
Published: (2025)

A Random Matrix Theory Perspective on the Learning Dynamics of Multi-head Latent Attention
by: Jha, Nandan Kumar, et al.
Published: (2025)

Efficient Discrepancy Testing for Learning with Distribution Shift
by: Chandrasekaran, Gautam, et al.
Published: (2024)

On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance
by: Zhang, Guoqiang
Published: (2023)

Softmax as Linear Attention in the Large-Prompt Regime: a Measure-based Perspective
by: Boursier, Etienne, et al.
Published: (2025)