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 3871–3880 of 4891 papers

TitleStatusHype
Generative Archimedean CopulasCode0
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferenceCode0
PrivateMail: Supervised Manifold Learning of Deep Features With Differential Privacy for Image Retrieval—0
Sequence-based deep learning antibody design for in silico antibody affinity maturation—0
Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTMCode1
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 Fundamental Limits of Exact Inference in Structured Prediction—0
On the Post-hoc Explainability of Deep Echo State Networks for Time Series Forecasting, Image and Video Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified