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 3961–3970 of 4891 papers

TitleStatusHype
Computationally Efficient Learning of Statistical ManifoldsCode0
Sequence-based deep learning antibody design for in silico antibody affinity maturation—0
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning—0
The Variational Bayesian Inference for Network Autoregression Models—0
RFI Mitigation for One-bit UWB Radar Systems—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
On the Fundamental Limits of Exact Inference in Structured Prediction—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified