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

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Showing 4351–4375 of 4891 papers

TitleStatusHype
Trade-offs in Large-Scale Distributed Tuplewise Estimation and LearningCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Object Placement on Cluttered Surfaces: A Nested Local Search Approach—0
Dual-control based approach to batch process operation under uncertainty based on optimality-conditions parameterization—0
Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation—0
Differentiable probabilistic models of scientific imaging with the Fourier slice theoremCode0
Imitation Learning of Neural Spatio-Temporal Point ProcessesCode0
Warping Resilient Scalable Anomaly Detection in Time Series—0
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing NetworkCode0
Learning Gaussian Graphical Models with Ordered Weighted L1 RegularizationCode0
A Robust Roll Angle Estimation Algorithm Based on Gradient Descent—0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
Self-Attentional Models for Lattice Inputs—0
3D Shape Reconstruction From Images in the Frequency Domain—0
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler—0
Learning Navigation Subroutines from Egocentric Videos—0
The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation—0
Scaling Video Analytics on Constrained Edge NodesCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics—0
Compression with Flows via Local Bits-Back CodingCode0
Prototypical Bregman Networks—0
Dynamic Neural Network Channel Execution for Efficient Training—0
Dynamically Expanded CNN Array for Video Coding—0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified