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 4826–4850 of 4891 papers

TitleStatusHype
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field ModelCode0
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance RankingCode0
AdaBatch: Adaptive Batch Sizes for Training Deep Neural NetworksCode0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language ModelsCode0
Adjusted chi-square test for degree-corrected block modelsCode0
Graph-Based Representation Learning of Neuronal Dynamics and BehaviorCode0
ATLAS: Adapting Trajectory Lengths and Step-Size for Hamiltonian Monte CarloCode0
Fast and Accurate Amplitude Demodulation of Wideband SignalsCode0
UNetVL: Enhancing 3D Medical Image Segmentation with Chebyshev KAN Powered Vision-LSTMCode0
CCNet: Criss-Cross Attention for Semantic SegmentationCode0
A tensor network approach for chaotic time series predictionCode0
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard ModelCode0
Never Mind The No-Ops: Faster and Less Volatile Simulation Modelling of Co-Evolutionary Species Interactions via Spatial Cyclic GamesCode0
Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization TasksCode0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
AMMUNet: Multi-Scale Attention Map Merging for Remote Sensing Image SegmentationCode0
Enhancing Character-Level Understanding in LLMs through Token Internal Structure LearningCode0
Deep neural networks with controlled variable selection for the identification of putative causal genetic variantsCode0
BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM InferenceCode0
A differentiable programming framework for spin modelsCode0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified