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

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Showing 2711–2720 of 4891 papers

TitleStatusHype
Reachability Analysis Using Constrained Polynomial Logical ZonotopesCode0
Transformers-based architectures for stroke segmentation: A review—0
Retentive Decision Transformer with Adaptive Masking for Reinforcement Learning based Recommendation Systems—0
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation—0
Tiny Models are the Computational Saver for Large ModelsCode0
CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Model—0
Benchmarking Video Frame Interpolation—0
Understanding the Functional Roles of Modelling Components in Spiking Neural Networks—0
Stochastic parameter reduced-order model based on hybrid machine learning approaches—0
A Unified Module for Accelerating STABLE-DIFFUSION: LCM-LORA—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified