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 1401–1410 of 4891 papers

TitleStatusHype
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
DualView: Data Attribution from the Dual PerspectiveCode0
Adaptive Data Exploitation in Deep Reinforcement LearningCode0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
From Roots to Rewards: Dynamic Tree Reasoning with RLCode0
Identification of stormwater control strategies and their associated uncertainties using Bayesian OptimizationCode0
A Survey on Large-scale Machine LearningCode0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
A Cascaded Dilated Convolution Approach for Mpox Lesion ClassificationCode0
Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNsCode0
Show:102550
← PrevPage 141 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified