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 1276–1300 of 4891 papers

TitleStatusHype
An Explainable Reconfiguration-Based Optimization Algorithm for Industrial and Reliability-Redundancy Allocation Problems—0
FlowMotion: Target-Predictive Conditional Flow Matching for Jitter-Reduced Text-Driven Human Motion Generation—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
SpikeSift: A Computationally Efficient and Drift-Resilient Spike Sorting Algorithm—0
Is Temporal Prompting All We Need For Limited Labeled Action Recognition?—0
ToolACE-R: Tool Learning with Adaptive Self-Refinement—0
UniViTAR: Unified Vision Transformer with Native Resolution—0
LLMPi: Optimizing LLMs for High-Throughput on Raspberry Pi—0
Test-time Adaptation for Foundation Medical Segmentation Model without Parametric Updates—0
Revisiting Funnel Transformers for Modern LLM Architectures with Comprehensive Ablations in Training and Inference Configurations—0
Robust Channel Estimation for Optical Wireless Communications Using Neural NetworkCode0
FLAMES: A Hybrid Spiking-State Space Model for Adaptive Memory Retention in Event-Based Learning—0
SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching—0
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks—0
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning—0
Benchmarking Federated Machine Unlearning methods for Tabular Data—0
Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing—0
Efficient n-body simulations using physics informed graph neural networks—0
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization—0
DynMoLE: Boosting Mixture of LoRA Experts Fine-Tuning with a Hybrid Routing MechanismCode0
On the Steady-State Distributionally Robust Kalman FilterCode0
CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching—0
Tree-Guided L_1-Convex ClusteringCode0
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables—0
SU-YOLO: Spiking Neural Network for Efficient Underwater Object DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified