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 3201–3225 of 4891 papers

TitleStatusHype
Wait-Less Offline Tuning and Re-solving for Online Decision Making—0
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles—0
Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness—0
Warping Resilient Scalable Anomaly Detection in Time Series—0
Wasserstein Adaptive Value Estimation for Actor-Critic Reinforcement Learning—0
Wasserstein Distributionally Robust Control and State Estimation for Partially Observable Linear Systems—0
Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups—0
WCCNet: Wavelet-integrated CNN with Crossmodal Rearranging Fusion for Fast Multispectral Pedestrian Detection—0
Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity—0
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation—0
What Truly Matters in Trajectory Prediction for Autonomous Driving?—0
When and why are log-linear models self-normalizing?—0
When approximate design for fast homomorphic computation provides differential privacy guarantees—0
When Cloud Removal Meets Diffusion Model in Remote Sensing—0
When Molecular GAN Meets Byte-Pair Encoding—0
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods—0
Which price to pay? Auto-tuning building MPC controller for optimal economic cost—0
whittlehurst: A Python package implementing Whittle's likelihood estimation of the Hurst exponent—0
Whole-brain substitute CT generation using Markov random field mixture models—0
Why does Negative Sampling not Work Well? Analysis of Convexity in Negative Sampling—0
Why Size Matters: Feature Coding as Nystrom Sampling—0
Will Bilevel Optimizers Benefit from Loops—0
WITCHcraft: Efficient PGD attacks with random step size—0
WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care—0
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified