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 1591–1600 of 4891 papers

TitleStatusHype
A few-shot Label Unlearning in Vertical Federated Learning—0
fastHDMI: Fast Mutual Information Estimation for High-Dimensional Data—0
Ada-K Routing: Boosting the Efficiency of MoE-based LLMs—0
Large Language Model Evaluation via Matrix Nuclear-NormCode0
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
α-DPO: Adaptive Reward Margin is What Direct Preference Optimization NeedsCode1
Echo State Networks for Spatio-Temporal Area-Level Data—0
Large-scale Multi-objective Feature Selection: A Multi-phase Search Space Shrinking Approach—0
Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning—0
Large Scale Longitudinal Experiments: Estimation and InferenceCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified