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 3651–3660 of 4891 papers

TitleStatusHype
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual LearningCode0
Multi-task nonparallel support vector machine for classification—0
Deep learning, stochastic gradient descent and diffusion maps—0
A Temporal-oriented Broadcast ResNet for COVID-19 Detection—0
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models—0
Adaptive Divergence-based Non-negative Latent Factor Analysis—0
On Affine Policies for Wasserstein Distributionally Robust Unit Commitment—0
Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system—0
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud to Edge—0
Straightening skewed markets with an index tracking optimizationless portfolio—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified