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 1881–1890 of 4891 papers

TitleStatusHype
A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing—0
SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems—0
Faster Diffusion Action Segmentation—0
Multiview learning with twin parametric margin SVMCode0
STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs—0
Real-time Hybrid System Identification with Online Deterministic Annealing—0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
NeuralFactors: A Novel Factor Learning Approach to Generative Modeling of Equities—0
Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified