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 921–930 of 4891 papers

TitleStatusHype
Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen BindingCode0
Jointly Efficient and Optimal Algorithms for Logistic BanditsCode0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference AggregationCode0
Classification of Gleason Grading in Prostate Cancer Histopathology Images Using Deep Learning Techniques: YOLO, Vision Transformers, and Vision MambaCode0
Kernel Manifold AlignmentCode0
CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMsCode0
CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimationCode0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Achieving the time of 1-NN, but the accuracy of k-NNCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified