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 1241–1250 of 4891 papers

TitleStatusHype
SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training—0
Deep Learning for Resilient Adversarial Decision Fusion in Byzantine Networks—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Core Context Aware Attention for Long Context Language Modeling—0
Efficient Object-centric Representation Learning with Pre-trained Geometric Prior—0
Accelerating Sparse Graph Neural Networks with Tensor Core Optimization—0
The dark side of the forces: assessing non-conservative force models for atomistic machine learningCode2
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
Acceleration and Parallelization Methods for ISRS EGN Model—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