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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3611–3620 of 4891 papers

TitleStatusHype
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
All Birds with One Stone: Multi-task Learning for Inference with One Forward Pass—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
Uncertainty quantification and inverse modeling for subsurface flow in 3D heterogeneous formations using a theory-guided convolutional encoder-decoder network—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
Offline Contextual Bandits for Wireless Network Optimization—0
Observation Error Covariance Specification in Dynamical Systems for Data assimilation using Recurrent Neural Networks—0
Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers—0
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender SystemsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified