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 826–850 of 4891 papers

TitleStatusHype
Continuous Normalizing Flows for Uncertainty-Aware Human Pose Estimation—0
4D Cardiac Ultrasound Standard Plane Location by Spatial-Temporal Correlation—0
Fast and Robust Matching for Multimodal Remote Sensing Image Registration—0
Approximating particle-based clustering dynamics by stochastic PDEs—0
Approximating DTW with a convolutional neural network on EEG data—0
A Comprehensive Survey on Architectural Advances in Deep CNNs: Challenges, Applications, and Emerging Research Directions—0
Context-Preserving Gradient Modulation for Large Language Models: A Novel Approach to Semantic Consistency in Long-Form Text Generation—0
Approximate XVA for European claims—0
Approximate Steepest Coordinate Descent—0
Aeroengine performance prediction using a physical-embedded data-driven method—0
Approximate Model-Based Diagnosis Using Greedy Stochastic Search—0
Aerodynamic and structural airfoil shape optimisation via Transfer Learning-enhanced Deep Reinforcement Learning—0
A Comprehensive Survey of Action Quality Assessment: Method and Benchmark—0
3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification—0
Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning—0
Approximately Aligned Decoding—0
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability—0
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images—0
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified