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 2111–2120 of 4891 papers

TitleStatusHype
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION—0
SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound GenerationCode2
PromptWizard: Task-Aware Prompt Optimization FrameworkCode7
An Innovative Networks in Federated Learning—0
Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed TomographyCode4
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language ModelsCode2
On Fairness of Low-Rank Adaptation of Large ModelsCode0
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMsCode0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified