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Found 9 papers, 4 papers with code
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
SkyMath: Technical Report
Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.
CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1. 7k elementary school-level math word problems with detailed annotations, source from actual Chinese workbooks and exams.
A Flexible Multi-Task Model for BERT Serving
In this demonstration, we present an efficient BERT-based multi-task (MT) framework that is particularly suitable for iterative and incremental development of the tasks.
Masked Conditional Random Fields for Sequence Labeling
Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems.
Convex recovery of tensors using nuclear norm penalization
The subdifferential of convex functions of the singular spectrum of real matrices has been widely studied in matrix analysis, optimization and automatic control theory.
An Overview of the Asymptotic Performance of the Family of the FastICA Algorithms
This contribution summarizes the results on the asymptotic performance of several variants of the FastICA algorithm.
A study of the fixed points and spurious solutions of the FastICA algorithm
In the first part of this work, we are interested in the relationship between demixing vectors, local optimizers of the contrast function and (attractive or unattractive) fixed points of FastICA algorithm.
A convergence and asymptotic analysis of the generalized symmetric FastICA algorithm
This contribution deals with the generalized symmetric FastICA algorithm in the domain of Independent Component Analysis (ICA).
