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 841–850 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
PET Tracer Separation Using Conditional Diffusion Transformer with Multi-latent Space LearningCode0
Optimal Depth of Neural Networks—0
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning—0
OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections—0
Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration—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
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified