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 3026–3050 of 4891 papers

TitleStatusHype
TinyMetaFed: Efficient Federated Meta-Learning for TinyML—0
Tiny-Toxic-Detector: A compact transformer-based model for toxic content detection—0
TLINet: Differentiable Neural Network Temporal Logic Inference—0
Token Dynamics: Towards Efficient and Dynamic Video Token Representation for Video Large Language Models—0
Token Fusion: Bridging the Gap between Token Pruning and Token Merging—0
ToolACE-R: Tool Learning with Adaptive Self-Refinement—0
Tool flank wear prediction using high-frequency machine data from industrial edge device—0
TopK Language Models—0
TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations—0
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials—0
To Repair or Not to Repair? Investigating the Importance of AB-Cycles for the State-of-the-Art TSP Heuristic EAX—0
Toward Efficient Automated Feature Engineering—0
Toward Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning—0
An unsupervised, open-source workflow for 2D and 3D building mapping from airborne LiDAR data—0
Towards a physically realistic computationally efficient DVS pixel model—0
Towards Artificial General or Personalized Intelligence? A Survey on Foundation Models for Personalized Federated Intelligence—0
Towards Assessing Deep Learning Test Input Generators—0
Towards a Systematic Computational Framework for Modeling Multi-Agent Decision-Making at Micro Level for Smart Vehicles in a Smart World—0
Towards a Theory of Intentions for Human-Robot Collaboration—0
Towards a variational Jordan-Lee-Preskill quantum algorithm—0
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration—0
Towards Compute-Optimal Transfer Learning—0
Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers—0
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified