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 4201–4225 of 4891 papers

TitleStatusHype
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis—0
Fast Sequence-Based Embedding with Diffusion GraphsCode1
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Context-Aware Cross-Attention for Skeleton-Based Human Action Recognition—0
On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods—0
A lightweight target detection algorithm based on Mobilenet Convolution—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
The gap between theory and practice in function approximation with deep neural networksCode1
One-Shot Coordination of First and Last Mode Transportation—0
Online Continual Learning from Imbalanced Data—0
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory—0
On the Iteration Complexity of Hypergradient Computations—0
Active Learning in Video Tracking—0
Structural plasticity on an accelerated analog neuromorphic hardware system—0
TRADI: Tracking deep neural network weight distributions for uncertainty estimation—0
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines—0
A posteriori Trading-inspired Model-free Time Series Segmentation—0
A hierarchical approach to deep learning and its application to tomographic reconstruction—0
LiteSeg: A Novel Lightweight ConvNet for Semantic SegmentationCode0
Queueing Analysis of GPU-Based Inference Servers with Dynamic Batching: A Closed-Form Characterization—0
CHIRRUP: a practical algorithm for unsourced multiple access—0
Randomized Exploration for Non-Stationary Stochastic Linear BanditsCode0
MDFN: Multi-Scale Deep Feature Learning Network for Object Detection—0
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features—0
Learning a Neural 3D Texture Space from 2D ExemplarsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified