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 2101–2110 of 4891 papers

TitleStatusHype
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data—0
LArctan-SKAN: Simple and Efficient Single-Parameterized Kolmogorov-Arnold Networks using Learnable Trigonometric FunctionCode0
Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs—0
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models—0
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classification—0
Taipan: Efficient and Expressive State Space Language Models with Selective Attention—0
GeoLoRA: Geometric integration for parameter efficient fine-tuning—0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
Exploring Tokenization Methods for Multitrack Sheet Music Generation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified