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 4351–4375 of 4891 papers

TitleStatusHype
ParetoTracker: Understanding Population Dynamics in Multi-objective Evolutionary Algorithms through Visual AnalyticsCode0
Parsing Tweets into Universal DependenciesCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
E-detectors: a nonparametric framework for sequential change detectionCode0
A framework for deep learning emulation of numerical models with a case study in satellite remote sensingCode0
Partially Stochastic Infinitely Deep Bayesian Neural NetworksCode0
Graph Learning from Data under Structural and Laplacian ConstraintsCode0
Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite ProgrammingCode0
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learningCode0
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language ModelsCode0
EDENet: Echo Direction Encoding Network for Place Recognition Based on Ground Penetrating RadarCode0
The Virtues of Laziness in Model-based RL: A Unified Objective and AlgorithmsCode0
PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithmsCode0
Controlling Participation in Federated Learning with FeedbackCode0
Stepwise Alignment for Constrained Language Model Policy OptimizationCode0
Bayesian Compression for Deep LearningCode0
Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional NetworksCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
Graph Degree Linkage: Agglomerative Clustering on a Directed GraphCode0
Path-integral molecular dynamics with actively-trained and universal machine learning force fieldsCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
PathMLP: Smooth Path Towards High-order HomophilyCode0
Graph Construction with Flexible Nodes for Traffic Demand PredictionCode0
Granular Ball Twin Support Vector MachineCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified