⭐️ Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph

🖥 Github: https://github.com/dosonleung/fasttog

📕 Paper: https://arxiv.org/abs/2501.14300v1

@Machine_learn

WILDCHAT-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training

🖥 Github: https://github.com/penfever/wildchat-50m

📕 Paper: https://arxiv.org/abs/2501.18511v1

🧠 Dataset: https://huggingface.co/collections/nyu-dice-lab/wildchat-50m-679a5df2c5967db8ab341ab7

@Machine_learn

با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد.

🔸🔻🔸🔻🔸🔻🔻
@Raminmousa

با عرض سلام نفر ٥ ام از پروژه جديدمون باقي مونده و ٦ جايگاه ديگه پر شدن.
امكان اموزش كامل كار
كدنويسي كار
نحوه جمع اوري داده ها
نگارش مقاله در اين كار وجود داره


Project Title: MedRec: Medical recommender system for image classification without retraining

Github: https://github.com/Ramin1Mousa/MedicalRec

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Impact factor: 20.8




🔺 5- 300$
جهت مشارکت می تونید به ایدی بنده پیام بدین.
@Raminmousa

LLMs can see and hear without any training

30 Jan 2025 · Kumar Ashutosh, Yossi Gandelsman, Xinlei Chen, Ishan Misra, Rohit Girdhar ·

We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively, eventually generating a solution to the task. This enables various applications that typically require training specialized models on task-specific data. In particular, we establish a new state-of-the-art on emergent zero-shot image, video and audio captioning. MILS seamlessly applies to media generation as well, discovering prompt rewrites to improve text-to-image generation, and even edit prompts for style transfer! Finally, being a gradient-free optimization approach, MILS can invert multimodal embeddings into text, enabling applications like cross-modal arithmetic.

Paper: https://arxiv.org/pdf/2501.18096v1.pdf

Code: https://github.com/facebookresearch/mils
✅
https://www.tg-me.com/deep_learning_proj