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 4226–4250 of 4891 papers

TitleStatusHype
On the Trend-corrected Variant of Adaptive Stochastic Optimization Methods—0
Adaptive Direction-Guided Structure Tensor Total Variation—0
A lightweight target detection algorithm based on Mobilenet Convolution—0
One-Shot Coordination of First and Last Mode Transportation—0
Online Continual Learning from Imbalanced Data—0
On the Iteration Complexity of Hypergradient Computations—0
Spectral Graph Matching and Regularized Quadratic Relaxations: Algorithm and Theory—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 hierarchical approach to deep learning and its application to tomographic reconstruction—0
A posteriori Trading-inspired Model-free Time Series Segmentation—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
Value-of-Information based Arbitration between Model-based and Model-free Control—0
Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback—0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
Learning Positive Functions with Pseudo Mirror Descent—0
Singleshot : a scalable Tucker tensor decomposition—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified