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

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Showing 351–360 of 4891 papers

TitleStatusHype
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Reconsidering the Performance of GAE in Link PredictionCode1
Energy-based physics-informed neural network for frictionless contact problems under large deformationCode1
Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money LaunderingCode1
Birdie: Advancing State Space Models with Reward-Driven Objectives and CurriculaCode1
A Walsh Hadamard Derived Linear Vector Symbolic ArchitectureCode1
Comparative Analysis of Demonstration Selection Algorithms for LLM In-Context LearningCode1
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?Code1
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent CodingCode1
Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and ReasoningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified