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 2651–2700 of 4891 papers

TitleStatusHype
Saliency Driven Object recognition in egocentric videos with deep CNN—0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
Sample and Computationally Efficient Stochastic Kriging in High Dimensions—0
Efficient Budget Allocation for Large-Scale LLM-Enabled Virtual Screening—0
Sampling Constrained Continuous Probability Distributions: A Review—0
Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation—0
Sampling Strategies for Real-Time Action Recognition—0
Sampling Techniques for Streaming Cross Document Coreference Resolution—0
SAND: One-Shot Feature Selection with Additive Noise Distortion—0
SANN-PSZ: Spatially Adaptive Neural Network for Head-Tracked Personal Sound Zones—0
SAR Image Despeckling Using Quadratic-Linear Approximated L1-Norm—0
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus—0
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models—0
Scalable Co-Clustering for Large-Scale Data through Dynamic Partitioning and Hierarchical Merging—0
Scalable computation of prediction intervals for neural networks via matrix sketching—0
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Scalable Global Solution Techniques for High-Dimensional Models in Dynare—0
Scalable imputation of genetic data with a discrete fragmentation-coagulation process—0
Scalable Machine Learning Algorithms using Path Signatures—0
Scalable Non-linear Learning with Adaptive Polynomial Expansions—0
Scalable Nonlinear Learning with Adaptive Polynomial Expansions—0
Scalable Smartphone Cluster for Deep Learning—0
Scalable Subsampling Inference for Deep Neural Networks—0
Scalable Vehicle Re-Identification via Self-Supervision—0
Scale-free Unconstrained Online Learning for Curved Losses—0
ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion—0
Scaling Bayesian inference of mixed multinomial logit models to very large datasets—0
Scaling Continuous Kernels with Sparse Fourier Domain Learning—0
Scaling Laws for Sparsely-Connected Foundation Models—0
SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
Scattering and Gathering for Spatially Varying Blurs—0
Scatter Matrix Concordance: A Diagnostic for Regressions on Subsets of Data—0
Schreier-Coset Graph Propagation—0
SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR—0
SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)—0
SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds—0
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging—0
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles—0
Search-contempt: a hybrid MCTS algorithm for training AlphaZero-like engines with better computational efficiency—0
Searching for COMETINHO: The Little Metric That Could—0
Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling—0
Seesaw: High-throughput LLM Inference via Model Re-sharding—0
SegINR: Segment-wise Implicit Neural Representation for Sequence Alignment in Neural Text-to-Speech—0
Segment Any Crack: Deep Semantic Segmentation Adaptation for Crack Detection—0
Segregation and Context Aggregation Network for Real-time Cloud Segmentation—0
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain—0
Seismic wave propagation and inversion with Neural Operators—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified