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 926–950 of 4891 papers

TitleStatusHype
Provably Efficient RLHF Pipeline: A Unified View from Contextual Bandits—0
Latent Convergence Modulation in Large Language Models: A Novel Approach to Iterative Contextual Realignment—0
Calibrating LLMs with Information-Theoretic Evidential Deep LearningCode1
Smell of Source: Learning-Based Odor Source Localization with Molecular Communication—0
Inventory Consensus Control in Supply Chain Networks using Dissipativity-Based Control and Topology Co-Design—0
Bayesian Optimization by Kernel Regression and Density-based Exploration—0
Kolmogorov-Arnold Fourier Networks—0
Hierarchical Lexical Manifold Projection in Large Language Models: A Novel Mechanism for Multi-Scale Semantic Representation—0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Graph Neural Network Enabled Pinching Antennas—0
CluStRE: Streaming Graph Clustering with Multi-Stage Refinement—0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural NetworksCode0
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video GenerationCode3
Flopping for FLOPs: Leveraging equivariance for computational efficiency—0
Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures—0
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient—0
Tighter sparse variational Gaussian processes—0
Hierarchical Contextual Manifold Alignment for Structuring Latent Representations in Large Language Models—0
Gaussian Process Regression for Inverse Problems in Linear PDEs—0
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation—0
OneTrack-M: A multitask approach to transformer-based MOT models—0
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers—0
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified