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 841–850 of 4891 papers

TitleStatusHype
Approximately Aligned Decoding—0
Approximate Dynamic Programming for Constrained Piecewise Affine Systems with Stability and Safety Guarantees—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability—0
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images—0
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
Applications of ML-Based Surrogates in Bayesian Approaches to Inverse Problems—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified