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 1671–1680 of 4891 papers

TitleStatusHype
When Molecular GAN Meets Byte-Pair Encoding—0
Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks—0
Efficient Semantic Diffusion Architectures for Model Training on Synthetic EchocardiogramsCode0
On the Power of Decision Trees in Auto-Regressive Language Modeling—0
Analysis of Truncated Singular Value Decomposition for Koopman Operator-Based Lane Change Model—0
TensorSocket: Shared Data Loading for Deep Learning Training—0
Grid Evolution for Doubly Fractional Channel Estimation in OTFS Systems—0
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction—0
Conjugate Bayesian Two-step Change Point Detection for Hawkes ProcessCode0
MoJE: Mixture of Jailbreak Experts, Naive Tabular Classifiers as Guard for Prompt Attacks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified