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 2581–2590 of 4891 papers

TitleStatusHype
Learning to sample in Cartesian MRI—0
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization ApproachCode1
Time-Domain Operational Metrics for Real-time Resilience Assessment in DC Microgrids—0
Differentiable Point-based Inverse Rendering—0
SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation SystemCode2
PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-OffCode0
Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning—0
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
On Significance of Subword tokenization for Low Resource and Efficient Named Entity Recognition: A case study in Marathi—0
Token Fusion: Bridging the Gap between Token Pruning and Token Merging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified