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 561–570 of 4891 papers

TitleStatusHype
High Dimensional Bayesian Optimization using Lasso Variable SelectionCode0
An Explainable Reconfiguration-Based Optimization Algorithm for Industrial and Reliability-Redundancy Allocation Problems—0
Representing Flow Fields with Divergence-Free Kernels for Reconstruction—0
UniViTAR: Unified Vision Transformer with Native Resolution—0
SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection—0
SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching—0
Benchmarking Federated Machine Unlearning methods for Tabular Data—0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified