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 3201–3250 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
XPose: eXplainable Human Pose Estimation—0
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
XVoxel-Based Parametric Design Optimization of Feature Models—0
Y-MAP-Net: Real-time depth, normals, segmentation, multi-label captioning and 2D human pose in RGB images—0
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series—0
YOLOv12: A Breakdown of the Key Architectural Features—0
YOLOv8-Based Visual Detection of Road Hazards: Potholes, Sewer Covers, and Manholes—0
YotoR-You Only Transform One Representation—0
You are out of context!—0
ZeroLM: Data-Free Transformer Architecture Search for Language Models—0
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats—0
Zero-Shot Detection of AI-Generated Images—0
Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment—0
Zero-Shot Multi-task Hallucination Detection—0
ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding—0
1.58-bit FLUX—0
Improving Harmful Text Detection with Joint Retrieval and External Knowledge—0
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning—0
Improving LoRA in Privacy-preserving Federated Learning—0
Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics—0
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
Improving Prostate Gland Segmenting Using Transformer based Architectures—0
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs—0
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified