Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows
📄 Book
@Machine_learn
📄 Book
@Machine_learn
❤1
The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise
tldr: Person with AI ~ Person who talks and works with teammates.
Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5188231
@Machine_learn
tldr: Person with AI ~ Person who talks and works with teammates.
Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5188231
@Machine_learn
Vine Copulas as Differentiable Computational Graphs
🖥 Github: https://github.com/TY-Cheng/torchvinecopulib
📕 Paper: https://arxiv.org/abs/2506.13318v1
🔗 Tasks: https://paperswithcode.com/task/scheduling
@Machine_learn
🖥 Github: https://github.com/TY-Cheng/torchvinecopulib
📕 Paper: https://arxiv.org/abs/2506.13318v1
🔗 Tasks: https://paperswithcode.com/task/scheduling
@Machine_learn
UniFork: Exploring Modality Alignment for Unified Multimodal Understanding and Generation
🖥 Github: https://github.com/tliby/unifork
📕 Paper: https://arxiv.org/abs/2506.17202v1
🔗 Dataset: https://paperswithcode.com/dataset/gqa
@Machine_learn
🖥 Github: https://github.com/tliby/unifork
📕 Paper: https://arxiv.org/abs/2506.17202v1
🔗 Dataset: https://paperswithcode.com/dataset/gqa
@Machine_learn
❤1
Forwarded from Papers
سلام دوستانی که مقالات مرتبط با یادگیری ماشین و یادگیری عمیق دارند و می خوان افرادی در مقالاتشون مشارکت کنند با رعایت برخی شرایط می تونن در کانالهای ما مقالاتشون رو ثبت کنن.
@Raminmousa
@Machine_learn
@paper4money
@Raminmousa
@Machine_learn
@paper4money
TCANet for motor imagery EEG classification
🖥 Github: https://github.com/tliby/unifork
📕 Paper: https://link.springer.com/article/10.1007/s11571-025-10275-5
🔗 Dataset: https://paperswithcode.com/task/brain-computer-interface
@Machine_learn
🖥 Github: https://github.com/tliby/unifork
📕 Paper: https://link.springer.com/article/10.1007/s11571-025-10275-5
🔗 Dataset: https://paperswithcode.com/task/brain-computer-interface
@Machine_learn
Forwarded from Papers
با عرض سلام نفرات ٢ تا ٤ قابل اضافه شدن به مقاله زير مي باشد.
Title:Probability latent for Recurrent Neural Networks Basic deficiencies
abstract:
Time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of the time concept in deep recurrent networks has led to more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of back-propagation. The problem of selecting feature order is an NP-complete problem. . ..... The proposed approach has an improvement of 0.49 over the reviewed approaches in some benchmarks.
Price:
2:500$
3:400$
4:250$
@Raminmousa
@Machine_learn
@Paper4money
Title:Probability latent for Recurrent Neural Networks Basic deficiencies
abstract:
Time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of the time concept in deep recurrent networks has led to more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of back-propagation. The problem of selecting feature order is an NP-complete problem. . ..... The proposed approach has an improvement of 0.49 over the reviewed approaches in some benchmarks.
Price:
2:500$
3:400$
4:250$
@Raminmousa
@Machine_learn
@Paper4money
❤1