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 4726–4750 of 4891 papers

TitleStatusHype
Fast clustering for scalable statistical analysis on structured images—0
Efficient Training of Very Deep Neural Networks for Supervised Hashing—0
Sequential Optimization for Efficient High-Quality Object Proposal Generation—0
Hierarchical Variational ModelsCode0
Symmetry-invariant optimization in deep networks—0
Understanding symmetries in deep networks—0
TiMEx: A Waiting Time Model for Mutually Exclusive Groups of Cancer Alterations—0
Robust Partially-Compressed Least-Squares—0
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion—0
Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network—0
Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse SupportCode0
Learning Wake-Sleep Recurrent Attention Models—0
Projection Bank: From High-dimensional Data to Medium-length Binary Codes—0
Sensor Selection by Linear Programming—0
Vision-Based Road Detection using Contextual Blocks—0
A tree-based kernel for graphs with continuous attributes—0
Mining Combined Causes in Large Data Sets—0
Places205-VGGNet Models for Scene RecognitionCode0
Compact Convolutional Neural Network Cascade for Face DetectionCode0
DNA cyclization and looping in the wormlike limit: normal modes and the validity of the harmonic approximation—0
Real-time 2D/3D Registration via CNN Regression—0
High Performance Computing of Gene Regulatory Networks using a Message-Passing Model—0
Gene expression modelling across multiple cell-lines with MapReduce—0
Multiscale Adaptive Representation of Signals: I. The Basic Framework—0
Scatter Matrix Concordance: A Diagnostic for Regressions on Subsets of Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified