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 3726–3750 of 4891 papers

TitleStatusHype
Noise Reduction and Driving Event Extraction Method for Performance Improvement on Driving Noise-based Surface Anomaly Detection—0
Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control—0
Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations—0
LSTM-based model predictive control with discrete inputs for irrigation scheduling—0
A Sparse Expansion For Deep Gaussian Processes—0
ST-MTL: Spatio-Temporal Multitask Learning Model to Predict Scanpath While Tracking Instruments in Robotic SurgeryCode0
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution—0
Dynamic Flow Equilibrium of Transportation and Power Distribution Networks Considering Flexible Traveling Choices and Voltage Quality Improvement—0
γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
Self-Organized Polynomial-Time Coordination GraphsCode0
Universalizing Weak Supervision—0
SHRIMP: Sparser Random Feature Models via Iterative Magnitude PruningCode0
Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision—0
Is Class-Incremental Enough for Continual Learning?—0
Self-Supervised Camera Self-Calibration from Video—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
U2-Former: A Nested U-shaped Transformer for Image Restoration—0
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency—0
Few-Shot Data-Driven Algorithms for Low Rank Approximation—0
CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded SystemsCode0
SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity MeasurementCode0
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Efficient Active Learning for Gaussian Process Classification by Error Reduction—0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified