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 3111–3120 of 4891 papers

TitleStatusHype
Fast Latent Factor Analysis via a Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm—0
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning MethodsCode1
TMHOI: Translational Model for Human-Object Interaction Detection—0
Iterative Approximate Cross-ValidationCode0
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition—0
CAMEL: Curvature-Augmented Manifold Embedding and Learning—0
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees—0
Switched Lyapunov Function based Controller Synthesis for Networked Control Systems: A Computationally Inexpensive Approach—0
Efficient Explorative Key-term Selection Strategies for Conversational Contextual BanditsCode0
The Virtues of Laziness in Model-based RL: A Unified Objective and AlgorithmsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified