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 1201–1210 of 4891 papers

TitleStatusHype
GAS: Generative Auto-bidding with Post-training Search—0
Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoising—0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba—0
Large Language Model Can Be a Foundation for Hidden Rationale-Based RetrievalCode0
Fed-ZOE: Communication-Efficient Over-the-Air Federated Learning via Zeroth-Order Estimation—0
A New Proof for the Linear Filtering and Smoothing Equations, and Asymptotic Expansion of Nonlinear Filtering—0
Real-Time Tea Leaf Disease Detection Using Deep Learning-Based ModelsCode0
A Computational Model of Learning and Memory Using Structurally Dynamic Cellular Automata—0
Optimization of Beyond Diagonal RIS: A Universal Framework Applicable to Arbitrary Architectures—0
Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-SolvingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified