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 951–960 of 4891 papers

TitleStatusHype
P-Hologen: An End-to-End Generative Framework for Phase-Only HologramsCode0
Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal TransportCode0
Compression with Flows via Local Bits-Back CodingCode0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference AggregationCode0
Learning to Reach Goals via DiffusionCode0
Learning to Select Pivotal Samples for Meta Re-weightingCode0
Heron Inference for Bayesian Graphical ModelsCode0
Leave-One-Out Stable Conformal PredictionCode0
Certified Error Control of Candidate Set Pruning for Two-Stage Relevance RankingCode0
Hierarchical Document Parsing via Large Margin Feature Matching and HeuristicsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified