From Deep Learning, Reinforcement Learning to Graph Neural Network"
Outline:
1. Understanding Core Concepts 2. Practical Application Skills 3. Research and Innovation Awareness 4. Problem-Solving with Advanced Techniques 5. Integration of Multidisciplinary Approaches
Abstract:
The workshop titled "New Approaches in Robotic Grasping: From Deep Learning, Reinforcement Learning to Graph Neural Networks" delves into the cutting-edge advancements in robotic grasp detection, a critical component for the automation of industrial tasks. Over the past six years, the Human-Robot Interaction Laboratory has focused on developing innovative solutions for grasp detection, utilizing a combination of geometric approaches and intelligent methods. This workshop will showcase the practical applications of these approaches through the development of a 3-DOF Delta robot and an advanced gripper. Attendees will gain insights into the integration of deep learning models for accurate and adaptive grasp detection, the application of reinforcement learning to enhance the decision-making capabilities of robotic systems, and the utilization of graph theory for optimizing grasp strategies and robot movements. In the workshop, we will also discuss how Transformers are revolutionizing robotic grasping and manipulation. These models, like GPT-4V and Gemini, enable complex robotic tasks with minimal training, using just input prompts. We'll review the latest developments in this area, including models like RT-1 and RT-2. By exploring these new approaches, participants will be equipped with the knowledge and tools necessary to advance the field of robotic grasping, contributing to the evolution of more autonomous and efficient robotic systems in industrial settings.
Presenters:
- Mehdi Tale Masouleh (Associate Professor at the University of Tehran)
- Hamed Hossieni (PhD Candidate at the University of Tehran)
- Hamed Ghasemi (PhD Candidate at the University of Tehran)
Access details:
This conference will be held virtually and is open to the public for free via this link.
Duration: 3 Hours
Date & Time: 19 November 2024 _ 13:00 - 16:00
"New Concepts in Large Language Models"
Outline:
1. Fine-tuning LLMs With Parameter-Efficient Fine-Tuning (PEFT) Methods 2. Enhancing Problem Solving in LLMs with Chain of Thought Reasoning 3. Using LLMs as Evaluative Tools for Human-Like Judgment 4. Improving Response Relevance in LLMs with Retrieval-Augmented Generation (RAG)
Abstract:
This workshop introduces advanced features of state-of-the-art large language models (LLMs), covering techniques to extend their problem solving, improve computational efficiency, increase the capability for evaluating information or reasonings (e.g. using chain- or tree-of-thought reasoning), and augment them with knowledge. Chain of thought (CoT) is a reasoning technique where large language models solve complex problems through step-by-step reasoning, enhancing the model's logic processing and allowing for more accurate output solutions. At the same time, Parameter-Efficient Fine-Tuning (PEFT) tackles the problem of adapting LLMs to down-stream tasks using fewer resources by covering methods that allow for LLM training with minimal computation ability. In addition, it talks about how we can use LLMs as judges or evaluators — judging responses and validating them from large datasets. This manifests in judgment and alignment with feedback at a general human level. In the last session, he introduces Retrieval-Augmented Generation (RAG), a way to combine retrieval (using IR) and generation of response based on-search to boost relevance & coverage. Combined, these sessions provide participants with an all-tools approach for advancing LLM capabilities across different AI applications using innovative techniques.
Presenters:
- Ali Moameri (Master's student at the Ferdowsi University of Mashhad) - Hossein Farahmand (Master's student at the Ferdowsi University of Mashhad) - Amirhossein Darmani (PhD Candidate at the Ferdowsi University of Mashhad) - Seyed Mohammad Feizabadi Sani (Master's student at the Ferdowsi University of Mashhad)
Access details:
The cost of attending workshop is 300,000 Tomans for the general public and 200,000 Tomans for students from all universities. For furthur information about registeration, you can visit this link.
