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 3001–3050 of 4891 papers

TitleStatusHype
The Role of Fibration Symmetries in Geometric Deep Learning—0
The Sample Complexity of Up-to- Multi-Dimensional Revenue Maximization—0
The Security Threat of Compressed Projectors in Large Vision-Language Models—0
These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion—0
The Significance of Machine Learning in Clinical Disease Diagnosis: A Review—0
The Solution for the AIGC Inference Performance Optimization Competition—0
The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics—0
The Symmetry of a Simple Optimization Problem in Lasso Screening—0
The thermodynamic efficiency of computations made in cells across the range of life—0
The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities—0
The use of deep learning in image segmentation, classification and detection—0
The Variational Bayesian Inference for Network Autoregression Models—0
The Weak Form Is Stronger Than You Think—0
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models—0
Think Beyond Size: Adaptive Prompting for More Effective Reasoning—0
Threshold Neuron: A Brain-inspired Artificial Neuron for Efficient On-device Inference—0
Threshold Strategy for Leaking Corner-Free Hamilton-Jacobi Reachability with Decomposed Computations—0
Tighter sparse variational Gaussian processes—0
Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition—0
TIM: An Efficient Temporal Interaction Module for Spiking Transformer—0
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence—0
Time-Domain Operational Metrics for Real-time Resilience Assessment in DC Microgrids—0
Scheduling HVAC loads to promote renewable generation integration with a learning-based joint chance-constrained approach—0
Time-Varying Dynamic Bayesian Networks—0
TiMEx: A Waiting Time Model for Mutually Exclusive Groups of Cancer Alterations—0
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