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 1421–1430 of 4891 papers

TitleStatusHype
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Fighting Randomness with Randomness: Mitigating Optimisation Instability of Fine-Tuning using Delayed Ensemble and Noisy InterpolationCode0
DeBaCl: A Python Package for Interactive DEnsity-BAsed CLusteringCode0
DCR: Quantifying Data Contamination in LLMs EvaluationCode0
Action Recognition Using Temporal Shift Module and Ensemble LearningCode0
Feed-Forward Optimization With Delayed Feedback for Neural NetworksCode0
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction NetworksCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified