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
Mojito: Motion Trajectory and Intensity Control for Video Generation—0
From Noise to Nuance: Advances in Deep Generative Image Models—0
evS2CP: Real-time Simultaneous Speed and Charging Planner for Connected Electric Vehicles—0
Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting—0
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLMCode1
New keypoint-based approach for recognising British Sign Language (BSL) from sequences—0
ProtoOcc: Accurate, Efficient 3D Occupancy Prediction Using Dual Branch Encoder-Prototype Query DecoderCode1
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach—0
Two-way Node Popularity Model for Directed and Bipartite NetworksCode0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs—0
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection—0
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements—0
Improving Satellite Imagery Masking using Multi-task and Transfer Learning—0
DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions—0
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications—0
Automatic Doubly Robust Forests—0
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems—0
Tazza: Shuffling Neural Network Parameters for Secure and Private Federated Learning—0
PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior GraphsCode1
Compression for Better: A General and Stable Lossless Compression Framework—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
Digital Twin-Empowered Voltage Control for Power Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified