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 4326–4350 of 4891 papers

TitleStatusHype
Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions—0
A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration—0
Bayesian Spatial Field Reconstruction with Unknown Distortions in Sensor Networks—0
Least Squares Approximation for a Distributed SystemCode0
A Machine Learning Based Classification Approach for Power Quality Disturbances Exploiting Higher Order Statistics in the EMD Domain—0
A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates—0
Point-Based Multi-View Stereo NetworkCode0
Continual Learning via Online Leverage Score Sampling—0
Towards a Theory of Intentions for Human-Robot Collaboration—0
Real-Time Correlation Tracking via Joint Model Compression and TransferCode0
LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually ImpairedCode0
Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices—0
Some New Results for Poisson Binomial Models—0
Spectral Graph Matching and Regularized Quadratic Relaxations I: The Gaussian Model—0
Super-Resolution Channel Estimation for Arbitrary Arrays in Hybrid Millimeter-Wave Massive MIMO Systems—0
Efficient Segmentation: Learning Downsampling Near Semantic BoundariesCode0
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning—0
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity RecognitionCode0
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling—0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
Quantum-inspired canonical correlation analysis for exponentially large dimensional dataCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
Learning to Generate Synthetic 3D Training Data through Hybrid Gradient—0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
Compositionally-Warped Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified