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 4576–4600 of 4891 papers

TitleStatusHype
DocParseNet: Advanced Semantic Segmentation and OCR Embeddings for Efficient Scanned Document AnnotationCode0
Computational Discovery of Chiasmus in Ancient Religious TextCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Compression with Flows via Local Bits-Back CodingCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Self-Organized Polynomial-Time Coordination GraphsCode0
Distribution-Free Predictive Inference For RegressionCode0
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural NetworkCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Distilling Textual Priors from LLM to Efficient Image FusionCode0
Style Interleaved Learning for Generalizable Person Re-identificationCode0
Auto-nnU-Net: Towards Automated Medical Image SegmentationCode0
Finding Influential Training Samples for Gradient Boosted Decision TreesCode0
A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind DeconvolutionCode0
Proteus: Preserving Model Confidentiality during Graph OptimizationsCode0
Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty Quantification for LoRACode0
Distance Metric Learning for Graph Structured DataCode0
Distance-Based Tree-Sliced Wasserstein DistanceCode0
ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse KinematicsCode0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modelingCode0
Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN NetworksCode0
When Mamba Meets xLSTM: An Efficient and Precise Method with the xLSTM-VMUNet Model for Skin lesion SegmentationCode0
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function ApproximationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified