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 2926–2950 of 4891 papers

TitleStatusHype
Generalized Random Forests using Fixed-Point TreesCode0
Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems—0
HabiCrowd: A High Performance Simulator for Crowd-Aware Visual NavigationCode1
RoMe: Towards Large Scale Road Surface Reconstruction via Mesh RepresentationCode2
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic ProgrammingCode0
Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference—0
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series—0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression—0
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm—0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction—0
Slicing Unbalanced Optimal TransportCode0
Efficiently Learning the Graph for Semi-supervised LearningCode0
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation—0
Learnable Digital Twin for Efficient Wireless Network EvaluationCode0
Audio-Visual Speech Enhancement With Selective Off-Screen Speech Extraction—0
From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer ParadigmCode1
A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution—0
Backup Plan Constrained Model Predictive Control with Guaranteed Stability—0
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms—0
An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention—0
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion EstimationCode0
Hexatagging: Projective Dependency Parsing as TaggingCode1
Safe Collaborative FilteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified