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 2391–2400 of 4891 papers

TitleStatusHype
Pose Magic: Efficient and Temporally Consistent Human Pose Estimation with a Hybrid Mamba-GCN Network—0
Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNsCode0
BOTS-LM: Training Large Language Models for Setswana—0
A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing—0
SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems—0
Faster Diffusion Action Segmentation—0
Multiview learning with twin parametric margin SVMCode0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
Real-time Hybrid System Identification with Online Deterministic Annealing—0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified