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 4301–4325 of 4891 papers

TitleStatusHype
BEBERT: Efficient and Robust Binary Ensemble BERTCode0
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity RecognitionCode0
Graph Self-Supervised Learning with Learnable Structural and Positional EncodingsCode0
AlgebraNetsCode0
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal TransportCode0
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data IntegrationCode0
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-ColoringsCode0
Cortical surface registration using unsupervised learningCode0
COrAL: Order-Agnostic Language Modeling for Efficient Iterative RefinementCode0
Coping With Simulators That Don't Always ReturnCode0
Converting Transformers into DGNNs FormCode0
Learning to Extract a Video Sequence from a Single Motion-Blurred ImageCode0
Efficient and principled score estimation with Nyström kernel exponential familiesCode0
Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield EnergyCode0
Efficient and Accurate Full-Waveform Inversion with Total Variation ConstraintCode0
Efficient Anatomical Labeling of Pulmonary Tree Structures via Deep Point-Graph Representation-based Implicit FieldsCode0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
Efficient Alternating Minimization Solvers for Wyner Multi-View Unsupervised LearningCode0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual LearningCode0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
Overlapping community detection in networks via sparse spectral decompositionCode0
Learning to Rank Using Localized Geometric Mean MetricsCode0
Learning to Reach Goals via DiffusionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified