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Found 6 papers, 2 papers with code
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion
Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate.
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight Prediction
The key insight of our proposal is that we employ a weight prediction strategy in the forward pass to ensure that each mini-batch uses consistent and staleness-free weights to compute the forward pass.
XPipe: Efficient Pipeline Model Parallelism for Multi-GPU DNN Training
It allows the overlapping of the pipelines of multiple micro-batches, including those belonging to different mini-batches.
An Efficient ADMM-Based Algorithm to Nonconvex Penalized Support Vector Machines
Support vector machines (SVMs) with sparsity-inducing nonconvex penalties have received considerable attentions for the characteristics of automatic classification and variable selection.
Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors
LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples.
On the Iteration Complexity Analysis of Stochastic Primal-Dual Hybrid Gradient Approach with High Probability
In this paper, we propose a stochastic Primal-Dual Hybrid Gradient (PDHG) approach for solving a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function.
