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 4501–4525 of 4891 papers

TitleStatusHype
Composite Marginal Likelihood Methods for Random Utility Models—0
HydraNets: Specialized Dynamic Architectures for Efficient Inference—0
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAMCode0
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical ModelsCode0
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and ApplicationsCode0
Core Conflictual Relationship: Text Mining to Discover What and When—0
Ergodic Inference: Accelerate Convergence by Optimisation—0
On the Estimation of Entropy in the FastICA AlgorithmCode0
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines—0
Exp-Concavity of Proper Composite Losses—0
Automated Process Planning for Hybrid Manufacturing—0
Multi-level Wavelet-CNN for Image RestorationCode0
Dyna: A Method of Momentum for Stochastic Optimization—0
Neural Open Information Extraction—0
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach—0
Parsing Tweets into Universal DependenciesCode0
High Performance Visual Tracking with Circular and Structural Operators—0
Is feature selection secure against training data poisoning?—0
Shapley Value Methods for Attribution Modeling in Online Advertising—0
SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality AssessmentCode0
A new robust feature selection method using variance-based sensitivity analysis—0
Fast Counting in Machine Learning ApplicationsCode0
Causal Generative Domain Adaptation Networks—0
Estimating Time-Varying Graphical ModelsCode0
Learning to Extract a Video Sequence from a Single Motion-Blurred ImageCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified