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 4021–4030 of 4891 papers

TitleStatusHype
DB-LLM: Accurate Dual-Binarization for Efficient LLMs—0
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer—0
DCE: Offline Reinforcement Learning With Double Conservative Estimates—0
DCF-ASN: Coarse-to-fine Real-time Visual Tracking via Discriminative Correlation Filter and Attentional Siamese Network—0
D-CODE: Data Colony Optimization for Dynamic Network Efficiency—0
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks—0
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
Decision Machines: Congruent Decision Trees—0
Decoder-only Architecture for Speech Recognition with CTC Prompts and Text Data Augmentation—0
Decouple to Reconstruct: High Quality UHD Restoration via Active Feature Disentanglement and Reversible Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified