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 3401–3410 of 4891 papers

TitleStatusHype
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Adversarial Imitation Learning via Random Search—0
Adversarial Purification for Data-Driven Power System Event Classifiers with Diffusion Models—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
Aeroengine performance prediction using a physical-embedded data-driven method—0
Fast and Robust Matching for Multimodal Remote Sensing Image Registration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified