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 3051–3075 of 4891 papers

TitleStatusHype
Dynamic Voxel Grid Optimization for High-Fidelity RGB-D Supervised Surface Reconstruction—0
DynamicDet: A Unified Dynamic Architecture for Object DetectionCode1
Gradient-Free Textual Inversion—0
GPr-Net: Geometric Prototypical Network for Point Cloud Few-Shot LearningCode1
State estimation of a carbon capture process through POD model reduction and neural network approximation—0
Scale-Space Hypernetworks for Efficient Biomedical Imaging—0
Machine learning for structure-property relationships: Scalability and limitations—0
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty—0
A Framework for Combustion Chemistry Acceleration with DeepONets—0
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment—0
Visualizing Skiers' Trajectories in Monocular Videos—0
When approximate design for fast homomorphic computation provides differential privacy guarantees—0
InterFormer: Real-time Interactive Image SegmentationCode1
Efficient CNNs via Passive Filter Pruning—0
Tractable Identification of Electric Distribution Networks—0
A differentiable programming framework for spin modelsCode0
Generative Multiplane Neural Radiance for 3D-Aware Image GenerationCode1
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior RefinementCode1
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations—0
Propagating Parameter Uncertainty in Power System Nonlinear Dynamic Simulations Using a Koopman Operator-Based Surrogate Model—0
Polarity is all you need to learn and transfer fasterCode0
Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised LearningCode0
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error—0
GP-PCS: One-shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian ManifoldsCode0
Heat flux for semi-local machine-learning potentialsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified