:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Tuli, Shikhar, Lin, Chi-Heng, Hsu, Yen-Chang, Jha, Niraj K., Shen, Yilin, Jin, Hongxia
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computation and Language
Accesso online:	https://arxiv.org/abs/2405.00888
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

MoDeGPT: Modular Decomposition for Large Language Model Compression
di: Lin, Chi-Heng, et al.
Pubblicazione: (2024)

FlexiGPT: Pruning and Extending Large Language Models with Low-Rank Weight Sharing
di: Smith, James Seale, et al.
Pubblicazione: (2025)

MossNet: Mixture of State-Space Experts is a Multi-Head Attention
di: Tuli, Shikhar, et al.
Pubblicazione: (2025)

DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
di: Cui, Zichen Jeff, et al.
Pubblicazione: (2024)

GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data
di: Belova, Margarita, et al.
Pubblicazione: (2025)

DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
di: Zheng, Zihao, et al.
Pubblicazione: (2025)

DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models
di: Gao, Shangqian, et al.
Pubblicazione: (2024)

Continual Diffusion with STAMINA: STack-And-Mask INcremental Adapters
di: Smith, James Seale, et al.
Pubblicazione: (2023)

Learning Interpretable Differentiable Logic Networks for Time-Series Classification
di: Yue, Chang, et al.
Pubblicazione: (2025)

Learning Interpretable Differentiable Logic Networks
di: Yue, Chang, et al.
Pubblicazione: (2024)

Learning Interpretable Differentiable Logic Networks for Tabular Regression
di: Yue, Chang, et al.
Pubblicazione: (2025)

Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
di: Smith, James Seale, et al.
Pubblicazione: (2023)

TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator
di: Lala, Sayeri, et al.
Pubblicazione: (2024)

Uncertainty-Aware Transformers: Conformal Prediction for Language Models
di: Vellore, Abhiram, et al.
Pubblicazione: (2026)

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
di: Wang, Hongjie, et al.
Pubblicazione: (2023)

HEED: Density-Weighted Residual Alignment for Hybrid Vision-Language Model Distillation
di: Liang, Yihao, et al.
Pubblicazione: (2026)

DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors
di: Li, Chia-Hao, et al.
Pubblicazione: (2023)

ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning
di: Gao, Shangqian, et al.
Pubblicazione: (2025)

METRIK: Measurement-Efficient Randomized Controlled Trials using Transformers with Input Masking
di: Lala, Sayeri, et al.
Pubblicazione: (2024)

Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience
di: Stephen, Jake, et al.
Pubblicazione: (2026)

Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations
di: Dedhia, Bhishma, et al.
Pubblicazione: (2024)

LinMU: Multimodal Understanding Made Linear
di: Wang, Hongjie, et al.
Pubblicazione: (2026)

Knowledge Graphs are Implicit Reward Models: Path-Derived Signals Enable Compositional Reasoning
di: Kansal, Yuval, et al.
Pubblicazione: (2026)

COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare
di: Li, Chia-Hao, et al.
Pubblicazione: (2024)

PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare
di: Li, Chia-Hao, et al.
Pubblicazione: (2024)

MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
di: Kallini, Julie, et al.
Pubblicazione: (2024)

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
di: Dedhia, Bhishma, et al.
Pubblicazione: (2025)

CONFINE: Conformal Prediction for Interpretable Neural Networks
di: Huang, Linhui, et al.
Pubblicazione: (2024)

FastMTP: Accelerating LLM Inference with Enhanced Multi-Token Prediction
di: Cai, Yuxuan, et al.
Pubblicazione: (2025)

AsymVLM: Asymmetric Token Pruning for Efficient Vision-Language Model Inference
di: Feng, Yilin, et al.
Pubblicazione: (2026)

Retraining-Free Merging of Sparse MoE via Hierarchical Clustering
di: Chen, I-Chun, et al.
Pubblicazione: (2024)

An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models
di: Chen, Liang, et al.
Pubblicazione: (2024)

DynaMoE: Dynamic Token-Level Expert Activation with Layer-Wise Adaptive Capacity for Mixture-of-Experts Neural Networks
di: Gülmez, Gökdeniz
Pubblicazione: (2026)

DEI: Diversity in Evolutionary Inference for Quality-Diversity Search
di: Donaghy, John, et al.
Pubblicazione: (2026)

HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
di: Lin, Wenxiang, et al.
Pubblicazione: (2025)

DynaSpec: Context-aware Dynamic Speculative Sampling for Large-Vocabulary Language Models
di: Zhang, Jinbin, et al.
Pubblicazione: (2025)

STAB: Speech Tokenizer Assessment Benchmark
di: Vashishth, Shikhar, et al.
Pubblicazione: (2024)

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
di: Liu, Enshu, et al.
Pubblicazione: (2026)

On the Fallacy of Global Token Perplexity in Spoken Language Model Evaluation
di: Hsu, Chan-Jan, et al.
Pubblicazione: (2026)

DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration
di: Zhang, Hanzhi, et al.
Pubblicazione: (2025)