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Found 16 papers, 5 papers with code
Two-stage Generative Question Answering on Temporal Knowledge Graph Using Large Language Models
Temporal knowledge graph question answering (TKGQA) poses a significant challenge task, due to the temporal constraints hidden in questions and the answers sought from dynamic structured knowledge.
TFDMNet: A Novel Network Structure Combines the Time Domain and Frequency Domain Features
To get the balance between the computation complexity and memory usage, we propose a new network structure, namely Time-Frequency Domain Mixture Network (TFDMNet), which combines the advantages of both convolution layers and EMLs.
A Unified Generative Framework based on Prompt Learning for Various Information Extraction Tasks
In this paper, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of Information Extraction.
Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models
These features make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism and tensor model parallelism, to achieve high training efficiency.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation
To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight.
Inertial Proximal Deep Learning Alternating Minimization for Efficient Neutral Network Training
In recent years, the Deep Learning Alternating Minimization (DLAM), which is actually the alternating minimization applied to the penalty form of the deep neutral networks training, has been developed as an alternative algorithm to overcome several drawbacks of Stochastic Gradient Descent (SGD) algorithms.
P-ADMMiRNN: Training RNN with Stable Convergence via An Efficient and Paralleled ADMM Approach
We also provide novel update rules and theoretical convergence analysis.
Event Arguments Extraction via Dilate Gated Convolutional Neural Network with Enhanced Local Features
However, the F-Score of event arguments extraction is much lower than that of event trigger extraction, i. e. in the most recent work, event trigger extraction achieves 80. 7%, while event arguments extraction achieves only 58%.
Exploring Pre-trained Language Models for Event Extraction and Generation
Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size.
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.
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs
The stationary point of Problem 2 is NOT the stationary point of Problem 1.
Non-ergodic Complexity of Convex Proximal Inertial Gradient Descents
The non-ergodic O(1/k) rate is proved for proximal inertial gradient descent with constant stepzise when the objective function is coercive.
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.
Learning for Disparity Estimation through Feature Constancy
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
Stochastic Primal-Dual Proximal ExtraGradient Descent for Compositely Regularized Optimization
This optimization model abstracts a number of important applications in artificial intelligence and machine learning, such as fused Lasso, fused logistic regression, and a class of graph-guided regularized minimization.
Semantic tracking: Single-target tracking with inter-supervised convolutional networks
Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking.
