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 51–60 of 4891 papers

TitleStatusHype
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs—0
Scalable Machine Learning Algorithms using Path Signatures—0
OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections—0
Optimal Depth of Neural Networks—0
PET Tracer Separation Using Conditional Diffusion Transformer with Multi-latent Space LearningCode0
Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration—0
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning—0
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks—0
Cross-Modal Obfuscation for Jailbreak Attacks on Large Vision-Language Models—0
Efficient Transformations in Deep Learning Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified