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 4276–4300 of 4891 papers

TitleStatusHype
Multiphase flow prediction with deep neural networks—0
SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT PerfusionCode0
Coping With Simulators That Don’t Always Return—0
Scale-Equivariant Steerable NetworksCode0
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation—0
Accelerated Structured Alternating Projections for Robust Spectrally Sparse Signal RecoveryCode0
Commonsense Knowledge Base Completion with Structural and Semantic ContextCode0
Neural Multisensory Scene InferenceCode0
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
Multiagent Rollout Algorithms and Reinforcement LearningCode0
Integrated Triaging for Fast Reading Comprehension—0
Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural NetworksCode0
Online Learning of Dynamical Systems: An Operator Theoretic Approach—0
A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation—0
Extreme Value k-means Clustering—0
Deep Auto-Deferring Policy for Combinatorial Optimization—0
Meta-Learning for Variational Inference—0
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications—0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology—0
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed—0
Augmented Memory for Correlation Filters in Real-Time UAV TrackingCode0
The column measure and Gradient-Free Gradient Boosting—0
Sparse Canonical Correlation Analysis via Concave Minimization—0
A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR DescriptorsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified