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 1571–1580 of 4891 papers

TitleStatusHype
The Paradox of Stochasticity: Limited Creativity and Computational Decoupling in Temperature-Varied LLM Outputs of Structured Fictional Data—0
Exploring Patterns Behind Sports—0
Mixed Integer Linear Programming for Active Contact Selection in Deep Brain Stimulation—0
Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks—0
Learning Inverse Laplacian Pyramid for Progressive Depth Completion—0
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Hierarchical Document Parsing via Large Margin Feature Matching and HeuristicsCode0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition—0
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive ForecastingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified