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 2401–2450 of 4891 papers

TitleStatusHype
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces—0
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein EmbeddingCode3
Global Safe Sequential Learning via Efficient Knowledge TransferCode0
A Self-supervised Pressure Map human keypoint Detection Approch: Optimizing Generalization and Computational Efficiency Across Datasets—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering—0
Tumor segmentation on whole slide images: training or prompting?—0
VOOM: Robust Visual Object Odometry and Mapping using Hierarchical LandmarksCode2
EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization—0
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML—0
Emulating the interstellar medium chemistry with neural operators—0
FiT: Flexible Vision Transformer for Diffusion ModelCode3
Network Inversion of Binarised Neural Nets—0
Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential MarketsCode0
DualView: Data Attribution from the Dual PerspectiveCode0
DB-LLM: Accurate Dual-Binarization for Efficient LLMs—0
Turn Waste into Worth: Rectifying Top-k Router of MoE—0
Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank BanditsCode0
FViT: A Focal Vision Transformer with Gabor FilterCode1
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications—0
PhaseEvo: Towards Unified In-Context Prompt Optimization for Large Language Models—0
Private PAC Learning May be Harder than Online Learning—0
Model Editing by Standard Fine-TuningCode1
Collaborative Learning with Different Labeling Functions—0
Generalizability of Mixture of Domain-Specific Adapters from the Lens of Signed Weight Directions and its Application to Effective Model Pruning—0
Closed-form Filtering for Non-linear Systems—0
Multi-Fidelity Methods for Optimization: A Survey—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Oracle-Efficient Differentially Private Learning with Public Data—0
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional SystemsCode0
Contextual Multinomial Logit Bandits with General Value Functions—0
Mercury: A Code Efficiency Benchmark for Code Large Language ModelsCode2
Anchor-based Large Language ModelsCode1
Accelerating Distributed Deep Learning using Lossless Homomorphic CompressionCode0
On Computationally Efficient Multi-Class Calibration—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Differentially Private Training of Mixture of Experts Models—0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data StreamsCode0
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive LossCode1
AttnLRP: Attention-Aware Layer-Wise Relevance Propagation for Transformers—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular FusionCode2
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
BEBLID: Boosted efficient binary local image descriptorCode2
On the Completeness of Invariant Geometric Deep Learning ModelsCode0
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image SegmentationCode4
Majority Kernels: An Approach to Leverage Big Model Dynamics for Efficient Small Model Training—0
Partially Stochastic Infinitely Deep Bayesian Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified