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 681–690 of 4891 papers

TitleStatusHype
Transformer-based Machine Learning for Fast SAT Solvers and Logic SynthesisCode1
Training Adaptive Computation for Open-Domain Question Answering with Computational ConstraintsCode1
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and RetrievalCode1
Obtaining Better Static Word Embeddings Using Contextual Embedding ModelsCode1
Sub-Character Tokenization for Chinese Pretrained Language ModelsCode1
Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image RecognitionCode1
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networksCode1
Vision Transformer for Fast and Efficient Scene Text RecognitionCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
Auction-Based Combinatorial Multi-Armed Bandit Mechanisms with Strategic ArmsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified