Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs
🖥 Github: https://github.com/reml-group/deliberation-on-priors
📕 Paper: https://arxiv.org/abs/2505.15210v1
@Machine_learn
🖥 Github: https://github.com/reml-group/deliberation-on-priors
📕 Paper: https://arxiv.org/abs/2505.15210v1
@Machine_learn
🎓Advanced Applications of Machine Learning in Bioinformatics
🗓Publish year: 2025
📎 Study thesis
@Machine_learn
🗓Publish year: 2025
📎 Study thesis
@Machine_learn
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
24 Apr 2025 · Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang ·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
24 Apr 2025 · Minju Seo, Jinheon Baek, Seongyun Lee, Sung Ju Hwang ·
Paper: https://arxiv.org/pdf/2504.17192v2.pdf
Code: https://github.com/going-doer/paper2code
@Machine_learn
TabSTAR: A Foundation Tabular Model With
Semantically Target-Aware Representations
📚 Paper
@Machine_learn
Semantically Target-Aware Representations
📚 Paper
@Machine_learn
COUNTING THE NUMBER OF Zp-AND Fp[t]-FIXED POINTS OF A DISCRETE DYNAMICAL
SYSTEM WITH APPLICATIONS FROM ARITHMETIC STATISTICS
📚 Read
@Machine_learn
SYSTEM WITH APPLICATIONS FROM ARITHMETIC STATISTICS
📚 Read
@Machine_learn
Forwarded from Github LLMs
Article Title:
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
PDF Download Link:
https://arxiv.org/pdf/2504.19254v2.pdf
GitHub:
• https://github.com/cvs-health/uqlm
Datasets:
• GSM8K
• SVAMP
• PopQA
@Machine_learn
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers
PDF Download Link:
https://arxiv.org/pdf/2504.19254v2.pdf
GitHub:
• https://github.com/cvs-health/uqlm
Datasets:
• GSM8K
• SVAMP
• PopQA
@Machine_learn
Good papers
Solving Video Inverse Problems Using Image Diffusion Models
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Century: A Framework and Dataset for Evaluating Ethical Contextualisation of Sensitive Images
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion
A Decade’s Battle on Dataset Bias: Are We There Yet?
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
@Machine_learn
Solving Video Inverse Problems Using Image Diffusion Models
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Century: A Framework and Dataset for Evaluating Ethical Contextualisation of Sensitive Images
No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models
Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance
How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion
A Decade’s Battle on Dataset Bias: Are We There Yet?
HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
@Machine_learn
arXiv.org
Solving Video Inverse Problems Using Image Diffusion Models
Recently, diffusion model-based inverse problem solvers (DIS) have emerged as state-of-the-art approaches for addressing inverse problems, including image super-resolution, deblurring, inpainting,...
Article Title:
s3: You Don't Need That Much Data to Train a Search Agent via RL
PDF Download Link:
https://arxiv.org/pdf/2505.14146v1.pdf
GitHub:
• https://github.com/pat-jj/s3
Datasets:
• Natural Questions
• TriviaQA
• HotpotQA
• MedQA
• PubMedQA
==================================
@Machine_learn
s3: You Don't Need That Much Data to Train a Search Agent via RL
PDF Download Link:
https://arxiv.org/pdf/2505.14146v1.pdf
GitHub:
• https://github.com/pat-jj/s3
Datasets:
• Natural Questions
• TriviaQA
• HotpotQA
• MedQA
• PubMedQA
==================================
@Machine_learn
Forwarded from Papers
با عرض سلام دو موضوع رو می خواهیم به صورت گروهی ادامه بدیم.
- survey on GAN methods for time series data generation
- survey on highlights the advantages of foundation in new learning methods for time series data
این دو مقاله به صورت جلسه ای برگزار میشه و هر هفته ۱.۵ ساعت تدریس رو خواهم داشت برای کسانی که می خوان شرکت کنند. هر مقاله ۶ نفر خواهد داشت و هزینه هر نفر ۲۰۰$ خواهد بود.
دوستانی که اولین مقالشون و یا میخوان داخل این مقالات شرکت کنند به ایدی بنده مراجعه کنند.
@Raminmousa
@Machine_learn
@Paper4money
- survey on GAN methods for time series data generation
- survey on highlights the advantages of foundation in new learning methods for time series data
این دو مقاله به صورت جلسه ای برگزار میشه و هر هفته ۱.۵ ساعت تدریس رو خواهم داشت برای کسانی که می خوان شرکت کنند. هر مقاله ۶ نفر خواهد داشت و هزینه هر نفر ۲۰۰$ خواهد بود.
دوستانی که اولین مقالشون و یا میخوان داخل این مقالات شرکت کنند به ایدی بنده مراجعه کنند.
@Raminmousa
@Machine_learn
@Paper4money
Machine learning books and papers pinned «با عرض سلام دو موضوع رو می خواهیم به صورت گروهی ادامه بدیم. - survey on GAN methods for time series data generation - survey on highlights the advantages of foundation in new learning methods for time series data این دو مقاله به صورت جلسه ای برگزار میشه…»