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 3711–3720 of 4891 papers

TitleStatusHype
Value-Agnostic Conversational Semantic Parsing—0
Beyond Sentence-Level End-to-End Speech Translation: Context Helps—0
Multi-scale Matching Networks for Semantic CorrespondenceCode1
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding—0
Learning Span-Level Interactions for Aspect Sentiment Triplet ExtractionCode1
Dissecting FLOPs along input dimensions for GreenAI cost estimationsCode0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
A Frequency-based Parent Selection for Reducing the Effect of Evaluation Time Bias in Asynchronous Parallel Multi-objective Evolutionary Algorithms—0
Dispatch of Virtual Inertia and Damping: Numerical Method with SDP and ADMM—0
Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signalingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified