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 301–310 of 4891 papers

TitleStatusHype
EfficientPose: Scalable single-person pose estimationCode1
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
AdaRank: Adaptive Rank Pruning for Enhanced Model MergingCode1
Adaptive wavelet distillation from neural networks through interpretationsCode1
Adaptive Transformers for Learning Multimodal RepresentationsCode1
Efficient and Effective Augmentation Strategy for Adversarial TrainingCode1
Efficient and Compact Convolutional Neural Network Architectures for Non-temporal Real-time Fire DetectionCode1
Efficient and Information-Preserving Future Frame Prediction and BeyondCode1
Efficient Aggregated Kernel Tests using Incomplete U-statisticsCode1
An adaptive augmented Lagrangian method for training physics and equality constrained artificial neural networksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified