SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2101–2125 of 4891 papers

TitleStatusHype
LArctan-SKAN: Simple and Efficient Single-Parameterized Kolmogorov-Arnold Networks using Learnable Trigonometric FunctionCode0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data—0
Multi-Agent Reinforcement Learning with Selective State-Space Models—0
Taipan: Efficient and Expressive State Space Language Models with Selective Attention—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
Exploring Tokenization Methods for Multitrack Sheet Music Generation—0
ExpertFlow: Optimized Expert Activation and Token Allocation for Efficient Mixture-of-Experts Inference—0
Automated Defect Detection and Grading of Piarom Dates Using Deep Learning—0
RE-tune: Incremental Fine Tuning of Biomedical Vision-Language Models for Multi-label Chest X-ray Classification—0
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional ElementsCode0
Combinatorial Logistic BanditsCode0
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning—0
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models—0
Linear Partial Gromov-Wasserstein EmbeddingCode0
Learning-to-Defer for Extractive Question Answering—0
LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model—0
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications—0
S-CFE: Simple Counterfactual ExplanationsCode0
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified