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 1581–1590 of 4891 papers

TitleStatusHype
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling—0
The Moral Case for Using Language Model Agents for Recommendation—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
OMCAT: Omni Context Aware Transformer—0
Rethinking Graph Transformer Architecture Design for Node Classification—0
Holistic Physics Solver: Learning PDEs in a Unified Spectral-Physical Space—0
Quadratic Gating Functions in Mixture of Experts: A Statistical Insight—0
Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence induced Loss Function and Dual Attention—0
Adaptive Data Optimization: Dynamic Sample Selection with Scaling LawsCode1
SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified