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 3861–3870 of 4891 papers

TitleStatusHype
Nested-block self-attention for robust radiotherapy planning segmentation—0
Learning-based Robust Motion Planning with Guaranteed Stability: A Contraction Theory Approach—0
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Physics-Informed Autoencoders—0
FAITH: Fast iterative half-plane focus of expansion estimation using event-based optic flowCode0
CausalX: Causal Explanations and Block Multilinear Factor Analysis—0
Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics—0
Anytime Sampling for Autoregressive Models via Ordered AutoencodingCode1
Solving high-dimensional parabolic PDEs using the tensor train formatCode1
Computationally Efficient Learning of Statistical ManifoldsCode0
You Only Compress Once: Optimal Data Compression for Estimating Linear ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified