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Found 13 papers, 7 papers with code
Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning
For DL training, we deployed the Fire-Flyer 2 with 10, 000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%.
DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
We introduce DeepSeek-Prover-V1. 5, an open-source language model designed for theorem proving in Lean 4, which enhances DeepSeek-Prover-V1 by optimizing both training and inference processes.
TSC: A Simple Two-Sided Constraint against Over-Smoothing
Meanwhile, the contrastive constraint, applied to the representation matrix's rows, enhances the discriminability of the nodes.
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.
Emoji Promotes Developer Participation and Issue Resolution on GitHub
In this paper, we study how emoji usage influences developer participation and issue resolution in virtual workspaces.
Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic
While many organizations have shifted to working remotely during the COVID-19 pandemic, how the remote workforce and the remote teams are influenced by and would respond to this and future shocks remain largely unknown.
Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub
This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers.
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
However, commonly used out-of-the-box sentiment analysis tools cannot obtain reliable results on SE tasks and the misunderstanding of technical jargon is demonstrated to be the main reason.
A First Look at Emoji Usage on GitHub: An Empirical Study
We find that emojis are used by a considerable proportion of GitHub users.
Computers and Society Software Engineering
Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification
To tackle this problem, cross-lingual sentiment classification approaches aim to transfer knowledge learned from one language that has abundant labeled examples (i. e., the source language, usually English) to another language with fewer labels (i. e., the target language).
