:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Yang, Leixin, Xiang, Yu
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Computation and Language
Acceso en línea:	https://arxiv.org/abs/2309.12689
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
por: Bhan, Milan, et al.
Publicado: (2024)

CAARMA: Class Augmentation with Adversarial Mixup Regularization
por: Baali, Massa, et al.
Publicado: (2025)

Label Smoothing Improves Gradient Ascent in LLM Unlearning
por: Pang, Zirui, et al.
Publicado: (2025)

Extracting Rule-based Descriptions of Attention Features in Transformers
por: Friedman, Dan, et al.
Publicado: (2025)

Attention Smoothing Is All You Need For Unlearning
por: Zade, Saleh Zare, et al.
Publicado: (2026)

SAP: Syntactic Attention Pruning for Transformer-based Language Models
por: Lee, Tzu-Yun, et al.
Publicado: (2025)

Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification
por: Gao, Yijie, et al.
Publicado: (2023)

SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe
por: Xiao, Yuxin, et al.
Publicado: (2024)

Out-of-Distribution Detection by Leveraging Between-Layer Transformation Smoothness
por: Jelenić, Fran, et al.
Publicado: (2023)

Gated Linear Attention Transformers with Hardware-Efficient Training
por: Yang, Songlin, et al.
Publicado: (2023)

LASER: Attention with Exponential Transformation
por: Duvvuri, Sai Surya, et al.
Publicado: (2024)

Generalized Probabilistic Attention Mechanism in Transformers
por: Heo, DongNyeong, et al.
Publicado: (2024)

Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers
por: Aggarwal, Shubham, et al.
Publicado: (2026)

PaTH Attention: Position Encoding via Accumulating Householder Transformations
por: Yang, Songlin, et al.
Publicado: (2025)

AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
por: Collins, Jeremy A., et al.
Publicado: (2025)

Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers
por: Bozic, Vukasin, et al.
Publicado: (2023)

Learning to Focus: Focal Attention for Selective and Scalable Transformers
por: Ram, Dhananjay, et al.
Publicado: (2025)

Improving Transformers with Dynamically Composable Multi-Head Attention
por: Xiao, Da, et al.
Publicado: (2024)

Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
por: Tian, Ye, et al.
Publicado: (2024)

Gradient-guided Attention Map Editing: Towards Efficient Contextual Hallucination Mitigation
por: Wang, Yu, et al.
Publicado: (2025)

Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed Knowledge Distillation
por: Nguyen, Hieu, et al.
Publicado: (2025)

Automatic Differential Diagnosis using Transformer-Based Multi-Label Sequence Classification
por: Sadi, Abu Adnan, et al.
Publicado: (2024)

Selective Attention Improves Transformer
por: Leviathan, Yaniv, et al.
Publicado: (2024)

Transformer Based Linear Attention with Optimized GPU Kernel Implementation
por: Gerami, Armin, et al.
Publicado: (2025)

The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs
por: Nawrot, Piotr, et al.
Publicado: (2025)

Faster Transformer Decoding: N-gram Masked Self-Attention
por: Chelba, Ciprian, et al.
Publicado: (2020)

Selective Attention: Enhancing Transformer through Principled Context Control
por: Zhang, Xuechen, et al.
Publicado: (2024)

Mechanism and Emergence of Stacked Attention Heads in Multi-Layer Transformers
por: Musat, Tiberiu
Publicado: (2024)

ComplexFormer: Disruptively Advancing Transformer Inference Ability via Head-Specific Complex Vector Attention
por: Shao, Jintian, et al.
Publicado: (2025)

Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing
por: Fayyazsanavi, Pooya, et al.
Publicado: (2024)

RecurFormer: Not All Transformer Heads Need Self-Attention
por: Yan, Ruiqing, et al.
Publicado: (2024)

Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
por: Brandon, William, et al.
Publicado: (2024)

Learning to Explain: Supervised Token Attribution from Transformer Attention Patterns
por: Mihaila, George
Publicado: (2026)

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer
por: Dong, Yihe, et al.
Publicado: (2025)

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

Detecting and Rectifying Noisy Labels: A Similarity-based Approach
por: Huu-Tien, Dang, et al.
Publicado: (2025)

Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning
por: Zhussip, Magauiya, et al.
Publicado: (2025)

Energy-Gated Attention: Spectral Salience as an Inductive Bias for Transformer Attention
por: Zeris, Athanasios
Publicado: (2026)

Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification
por: Dong, Shi, et al.
Publicado: (2024)

AttentionEngine: A Versatile Framework for Efficient Attention Mechanisms on Diverse Hardware Platforms
por: Chen, Feiyang, et al.
Publicado: (2025)