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 2531–2540 of 4891 papers

TitleStatusHype
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization—0
PromptCoT: Align Prompt Distribution via Adapted Chain-of-Thought—0
Diffusion Models, Image Super-Resolution And Everything: A Survey—0
Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learning—0
Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion DeblurringCode1
Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints—0
TinyGPT-V: Efficient Multimodal Large Language Model via Small BackbonesCode3
RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified