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 3391–3400 of 4891 papers

TitleStatusHype
Two-Stage Neural Contextual Bandits for Personalised News RecommendationCode0
Combining Retrospective Approximation with Importance Sampling for Optimising Conditional Value at Risk—0
TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting—0
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning—0
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape ModelsCode0
Efficient fine-grained road segmentation using superpixel-based CNN and CRF models—0
Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifold—0
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Efficient Aggregated Kernel Tests using Incomplete U-statisticsCode1
Fast Simulation of Particulate Suspensions Enabled by Graph Neural Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified