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 541–550 of 4891 papers

TitleStatusHype
RWKVTTS: Yet another TTS based on RWKV-7Code2
Mathematical Modeling of Option Pricing with an Extended Black-Scholes Framework—0
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data—0
Towards Assessing Deep Learning Test Input Generators—0
Integrating Identity-Based Identification against Adaptive Adversaries in Federated Learning—0
Re-thinking Temporal Search for Long-Form Video UnderstandingCode2
Analytical Discovery of Manifold with Machine Learning—0
Scaling Video-Language Models to 10K Frames via Hierarchical Differential DistillationCode2
Improving Harmful Text Detection with Joint Retrieval and External Knowledge—0
A Sensorimotor Vision Transformer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified