Sonmit

This discussion summarizes our findings on the impact of data pruning on classification bias and underscores the promise of methods like MetriQ

Addressing Unfairness in Data Pruning for Deep Learning

This paper is presented on the Adaptive-RAG framework, which improves QA accuracy and efficiency by adapting to query complexities.

Smart Queries, Smarter Answers: Adaptive-RAG in Action

Researchers are proposing a simple modification to standard ResNet architectures that substantially improves OoD performance on the DDU benchmark.

One Line of Code Can Make AI Models Faster and More Reliable

Explore the latest advancements in Stable Diffusion technology as presented by the Stability AI Applied Research team. 

Latest Advances in Stable Diffusion Technology

In this study, researchers propose a data quality control pipeline for federated fine-tuning of foundation models. 

Enhancing Data Quality: Conclusion and Future Work, and References

Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.

ChipNeMo: Domain-Adapted LLMs for Chip Design: Evaluations

ChipNeMo: Domain-Adapted LLMs for Chip Design: Dataset

ChipNeMo: Domain-Adapted LLMs for Chip Design: Discussion

ChipNeMo: Domain-Adapted LLMs for Chip Design: ChipNemo Domain Adaptation Methods

ChipNeMo: Domain-Adapted LLMs for Chip Design: Appendix

ChipNeMo: Domain-Adapted LLMs for Chip Design: Conclusions

ChipNeMo: Domain-Adapted LLMs for Chip Design: Abstract and Intro

ChipNeMo: Domain-Adapted LLMs for Chip Design: LLM Applications

ChipNeMo: Domain-Adapted LLMs for Chip Design: Related Works

ChipNeMo: Domain-Adapted LLMs for Chip Design: Acknowledgements, Contributions and References

Researchers introduce Deep Lake, an open-source lakehouse for deep learning, optimizing complex data storage and streaming for deep learning frameworks.

Deep Lake, a Lakehouse for Deep Learning: Conclusions, Acknowledgement, and References

Embark on a comprehensive exploration of AI dialog datasets, uncovering insights into errors in system utterances and user responses.

Conclusion and Beyond: Navigating the Landscape of Errors and Feedback in AI Conversations

The definitive list of resources for learning how deep learning works on any time scale

Deep Learning in an Hour, Day, Season, or Decade

A man when he lies down is reduced to 1/9 of his height.

On the Proportions and on the Movements of the Human Figure

NVIDIA’s GTC 2023 offers more than 650 special events, sessions, and expert panels across technologies, industries, and skill levels.

NVIDIA GTC 2023: The Future of Generative AI is Here  

“That is our goal, perhaps—I admit it—as far as science goes,” said the fair-haired student, rising to the challenge. “But there are things above science.”

A SLIP UNDER THE MICROSCOPE

Key methods to understanding and utilizing pandas

The Python Panda Package Tutorial

Python, Ruby, PHP, Perl - all these languages share similar age and features, yet one of them is way more popular than the others. In this article I explore why

Why Python is The 1st Language

Data science is the underlying force that is driving recent advances in artificial intelligence (AI), and machine learning (ML). This has lead to the enormous growth of ML libraries and made established programming languages like Python more popular than ever before.

Introduction to The Machine Learning Stack

Our solution is to generate donations to cancer charities, around the world, from regular web searches that our users are performing

SONMIT

sonmit.com

Since n.d.

Sonmit's stories on HackerNoon