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 991–1000 of 4891 papers

TitleStatusHype
Model-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces—0
LLM-Generated Heuristics for AI Planning: Do We Even Need Domain-Independence Anymore?—0
AlphaAdam:Asynchronous Masked Optimization with Dynamic Alpha for Selective UpdatesCode0
Contextually Structured Token Dependency Encoding for Large Language Models—0
A Hybrid Dynamic Subarray Architecture for Efficient DOA Estimation in THz Ultra-Massive Hybrid MIMO Systems—0
PDE-DKL: PDE-constrained deep kernel learning in high dimensionalityCode0
MatIR: A Hybrid Mamba-Transformer Image Restoration ModelCode1
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images—0
CrowdSplat: Exploring Gaussian Splatting For Crowd RenderingCode0
Action Recognition Using Temporal Shift Module and Ensemble LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified