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 1861–1870 of 4891 papers

TitleStatusHype
Core Context Aware Attention for Long Context Language Modeling—0
Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag DataCode0
The Open Source Advantage in Large Language Models (LLMs)—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
CNNtention: Can CNNs do better with Attention?Code0
Acceleration and Parallelization Methods for ISRS EGN Model—0
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior—0
SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep—0
Accelerating Sparse Graph Neural Networks with Tensor Core Optimization—0
Optimal Gradient Checkpointing for Sparse and Recurrent Architectures using Off-Chip Memory—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified