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 2851–2875 of 4891 papers

TitleStatusHype
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Statistical Significance of Feature Importance RankingsCode0
Adaptive Deep Learning for Efficient Visual Pose Estimation aboard Ultra-low-power Nano-drones—0
Linear Periodically Time-Variant Digital PLL Phase Noise Modeling Using Conversion Matrices and Uncorrelated UpsamplingCode0
Spectral Clustering for Discrete Distributions—0
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence—0
MambaByte: Token-free Selective State Space Model—0
Unified Spatio-Temporal Tri-Perspective View Representation for 3D Semantic Occupancy Prediction—0
RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing—0
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding—0
PlaceFormer: Transformer-based Visual Place Recognition using Multi-Scale Patch Selection and Fusion—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection—0
OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learning—0
MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation—0
DITTO: Diffusion Inference-Time T-Optimization for Music Generation—0
TIM: An Efficient Temporal Interaction Module for Spiking Transformer—0
A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications—0
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms—0
Identifying and Analyzing Task-Encoding Tokens in Large Language Models—0
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
Curriculum Recommendations Using Transformer Base Model with InfoNCE Loss And Language Switching Method—0
Inverse analysis of granular flows using differentiable graph neural network simulator—0
Functional Autoencoder for Smoothing and Representation LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified