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Found 14 papers, 5 papers with code
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion
To address the above issues, this paper proposes two novel path-based reasoning methods to solve the sparsity issues of entity and path respectively, which adopts the textual semantic information of entities and paths for MedKGC.
Joint Entity and Relation Extraction with Set Prediction Networks
Compared with cross-entropy loss that highly penalizes small shifts in triple order, the proposed bipartite matching loss is invariant to any permutation of predictions; thus, it can provide the proposed networks with a more accurate training signal by ignoring triple order and focusing on relation types and entities.
Ranked #1 on
Joint Entity and Relation Extraction
on NYT
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
Existing works didn{'}t consider the extraction order of relational facts in a sentence.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
The relational facts in sentences are often complicated.
Ranked #12 on
Relation Extraction
on NYT11-HRL
Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?
As one of the most important test of China, Gaokao is designed to be difficult enough to distinguish the excellent high school students.
The Ordered Weighted $\ell_1$ Norm: Atomic Formulation, Projections, and Algorithms
The ordered weighted $\ell_1$ norm (OWL) was recently proposed, with two different motivations: its good statistical properties as a sparsity promoting regularizer; the fact that it generalizes the so-called {\it octagonal shrinkage and clustering algorithm for regression} (OSCAR), which has the ability to cluster/group regression variables that are highly correlated.
Decreasing Weighted Sorted $\ell_1$ Regularization
We consider a new family of regularizers, termed {\it weighted sorted $\ell_1$ norms} (WSL1), which generalizes the recently introduced {\it octagonal shrinkage and clustering algorithm for regression} (OSCAR) and also contains the $\ell_1$ and $\ell_{\infty}$ norms as particular instances.
Exploiting Two-Dimensional Group Sparsity in 1-Bit Compressive Sensing
The subgradient of the 2D one-sided $\ell_1$ (or $\ell_2$) penalty and the projection onto the $K$-sparsity and TV or MTV constraint can be computed efficiently, allowing the appliaction of algorithms of the {\it forward-backward splitting} (a. k. a.
Group-sparse Matrix Recovery
We show that the proximity operator of 2OSCAR can be computed based on that of OSCAR.
Binary Fused Compressive Sensing: 1-Bit Compressive Sensing meets Group Sparsity
We propose a new method, {\it binary fused compressive sensing} (BFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements.
Robust Binary Fused Compressive Sensing using Adaptive Outlier Pursuit
We propose a new method, {\it robust binary fused compressive sensing} (RoBFCS), to recover sparse piece-wise smooth signals from 1-bit compressive measurements.
A novel sparsity and clustering regularization
We propose a novel SPARsity and Clustering (SPARC) regularizer, which is a modified version of the previous octagonal shrinkage and clustering algorithm for regression (OSCAR), where, the proposed regularizer consists of a $K$-sparse constraint and a pair-wise $\ell_{\infty}$ norm restricted on the $K$ largest components in magnitude.
Solving OSCAR regularization problems by proximal splitting algorithms
The OSCAR regularizer has a non-trivial proximity operator, which limits its applicability.
