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 3991–4000 of 4891 papers

TitleStatusHype
CSP-AIT-Net: A contrastive learning-enhanced spatiotemporal graph attention framework for short-term metro OD flow prediction with asynchronous inflow tracking—0
Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method—0
Curriculum reinforcement learning for quantum architecture search under hardware errors—0
Curvature Regularized Surface Reconstruction from Point Cloud—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automate—0
Cycle Consistent Probability Divergences Across Different Spaces—0
D4C Glove-train: Solving the RPM and Bongard-logo Problem by Circumscribing and Building Distribution for Concepts—0
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning—0
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified