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 751–760 of 4891 papers

TitleStatusHype
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
On the Iteration Complexity of Hypergradient ComputationCode1
Wasserstein Embedding for Graph LearningCode1
Augmented Sliced Wasserstein DistancesCode1
Exploring Quality and Generalizability in Parameterized Neural Audio EffectsCode1
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to EndCode1
Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity MaximizationCode1
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion InferenceCode1
Adaptive Transformers for Learning Multimodal RepresentationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified