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 4601–4625 of 4891 papers

TitleStatusHype
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
A non-alternating graph hashing algorithm for large scale image searchCode0
VORTEX: Challenging CNNs at Texture Recognition by using Vision Transformers with Orderless and Randomized Token EncodingsCode0
An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNetCode0
Statistical Significance of Feature Importance RankingsCode0
A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its BenefitsCode0
Achieving the time of 1-NN, but the accuracy of k-NNCode0
Dissecting FLOPs along input dimensions for GreenAI cost estimationsCode0
Pruning-Based TinyML Optimization of Machine Learning Models for Anomaly Detection in Electric Vehicle Charging InfrastructureCode0
Pruning Everything, Everywhere, All at OnceCode0
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inferenceCode0
ψDAG: Projected Stochastic Approximation Iteration for DAG Structure LearningCode0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and HypergraphsCode0
Comply: Learning Sentences with Complex Weights inspired by Fruit Fly OlfactionCode0
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action RecognitionCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer PruningCode0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation using Deformable Shape ModelsCode0
Dis-S2V: Discourse Informed Sen2VecCode0
MLRS-PDS: A Meta-learning recommendation of dynamic ensemble selection pipelinesCode0
MMD GAN: Towards Deeper Understanding of Moment Matching NetworkCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified