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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2341–2350 of 4891 papers

TitleStatusHype
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation—0
Deep Learning for In-Orbit Cloud Segmentation and Classification in Hyperspectral Satellite Data—0
Application of Distributed Arithmetic to Adaptive Filtering Algorithms: Trends, Challenges and Future—0
Efficient Language Model Architectures for Differentially Private Federated Learning—0
Harder Tasks Need More Experts: Dynamic Routing in MoE ModelsCode2
Monotone Individual Fairness—0
Fine-Grained Pillar Feature Encoding Via Spatio-Temporal Virtual Grid for 3D Object DetectionCode1
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language ModelsCode4
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation—0
Efficient dual-scale generalized Radon-Fourier transform detector family for long time coherent integration—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified