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

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Showing 1311–1320 of 4891 papers

TitleStatusHype
A tensor network approach for chaotic time series predictionCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential MarketsCode0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
A Temporal Linear Network for Time Series ForecastingCode0
Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character DecompositionCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
Incorporating brain-inspired mechanisms for multimodal learning in artificial intelligenceCode0
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle PhysicsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified