:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Pathak, Abhay Kumar, Chaubey, Mrityunjay, Gupta, Manjari
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Performance Computation
Online Access:	https://arxiv.org/abs/2411.18234
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Comparative Performance Analysis of Activation Functions for Cardiovascular Disease Detection Using ECG Images
by: Mrityunjay Chaubey, et al.
Published: (2025)

Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification
by: Kermani, Arshia, et al.
Published: (2025)

CDS4RAG: Cyclic Dual-Sequential Hyperparameter Optimization for RAG
by: Chen, Pengzhou, et al.
Published: (2026)

LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems
by: Wu, Siyu, et al.
Published: (2026)

AdaGradSelect: An adaptive gradient-guided layer selection method for efficient fine-tuning of SLMs
by: Kumar, Anshul, et al.
Published: (2025)

Towards Generalized Parameter Tuning in Coherent Ising Machines: A Portfolio-Based Approach
by: Hanyu, Tatsuro, et al.
Published: (2025)

An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference
by: Yao, Feiyu, et al.
Published: (2026)

Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making
by: Amujo, Oluyemi Enoch, et al.
Published: (2024)

A Dataset for Research on Water Sustainability
by: Gupta, Pranjol Sen, et al.
Published: (2024)

Model Compression and Efficient Inference for Large Language Models: A Survey
by: Wang, Wenxiao, et al.
Published: (2024)

CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
by: Zheng, Wenhao, et al.
Published: (2025)

Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning
by: Willemsen, Floris-Jan, et al.
Published: (2025)

QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
by: Lin, Yujun, et al.
Published: (2024)

Mixture of Experts with Mixture of Precisions for Tuning Quality of Service
by: Imani, HamidReza, et al.
Published: (2024)

PATCH: Learnable Tile-level Hybrid Sparsity for LLMs
by: Hourri, Younes, et al.
Published: (2025)

Profiling LoRA/QLoRA Fine-Tuning Efficiency on Consumer GPUs: An RTX 4060 Case Study
by: Avinash, MSR
Published: (2025)

Rapid Augmentations for Time Series (RATS): A High-Performance Library for Time Series Augmentation
by: Skaf, Wadie, et al.
Published: (2026)

Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents
by: Zhang, Qizheng, et al.
Published: (2025)

EcoSpa: Efficient Transformer Training with Coupled Sparsity
by: Xiao, Jinqi, et al.
Published: (2025)

A 4D Hybrid Algorithm to Scale Parallel Training to Thousands of GPUs
by: Singh, Siddharth, et al.
Published: (2023)

FlashSVD: Memory-Efficient Inference with Streaming for Low-Rank Models
by: Shao, Zishan, et al.
Published: (2025)

EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
by: Yi, Qingao, et al.
Published: (2025)

GreedySnake: Accelerating SSD-Offloaded LLM Training with Efficient Scheduling and Optimizer Step Overlapping
by: Yin, Yishu, et al.
Published: (2025)

Comment on paper: Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
by: Min, Yimeng
Published: (2024)

Training Time Prediction for Mixed Precision-based Distributed Training
by: Kang, Minchul, et al.
Published: (2026)

ProTrain: Efficient LLM Training via Memory-Aware Techniques
by: Yang, Hanmei, et al.
Published: (2024)

Prism: Unleashing GPU Sharing for Cost-Efficient Multi-LLM Serving
by: Yu, Shan, et al.
Published: (2025)

Revisiting 16-bit Neural Network Training: A Practical Approach for Resource-Limited Learning
by: Yun, Juyoung, et al.
Published: (2023)

QJL: 1-Bit Quantized JL Transform for KV Cache Quantization with Zero Overhead
by: Zandieh, Amir, et al.
Published: (2024)

MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark
by: Zhao, Qihao, et al.
Published: (2024)

Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations
by: Tyukin, Georgy
Published: (2024)

Hyperdimensional Computing for Sustainable Manufacturing: An Initial Assessment
by: Hoang, Danny, et al.
Published: (2025)

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
by: Chiang, Hung-Yueh, et al.
Published: (2025)

Energy-Aware LLMs: A step towards sustainable AI for downstream applications
by: Tran, Nguyen Phuc, et al.
Published: (2025)

An MLCommons Scientific Benchmarks Ontology
by: Hawks, Ben, et al.
Published: (2025)

REAM: Merging Improves Pruning of Experts in LLMs
by: Jha, Saurav, et al.
Published: (2026)

Data Efficacy for Language Model Training
by: Dai, Yalun, et al.
Published: (2025)

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search
by: Ellis-Mohr, Austin R., et al.
Published: (2025)

Bench360: Benchmarking Local LLM Inference from 360 Degrees
by: Stuhlmann, Linus, et al.
Published: (2025)

Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model
by: Baxi, Jatayu, et al.
Published: (2021)