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 4211–4220 of 4891 papers

TitleStatusHype
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified