2024 Intelligent Source Conference Agenda Revealed丨Generative Model

On June 14th and 15th, 2024, the 6th Beijing Zhiyuan Conference will be held offline and online It will be held in a combined online format, and the offline venue will be located at the Zhongguancun National Independent Innovation Demonstration Zone Conference Center. The 2024 Zhiyuan Conference once again brings together outstanding researchers of the year with a global perspective to exchange new ideas, explore new ideas, and lead new frontiers. Registration channels are now officially open.

##Countdown to Beijing Zhiyuan Conference: 11 Days

Generative Model Forum丨The afternoon of June 15

Generative functional modeling is one of the basic paradigms of artificial intelligence First, it is an important step towards general artificial intelligence. With the rapid development of generative modeling methods and the rapid growth of model scale, generative artificial intelligence (such as GPT series, Sora, Stable Diffusion, etc.) represented by autoregressive models and diffusion probability models has been widely used in text, images, videos, A series of breakthroughs have been made in important areas such as cross-modality. This forum focuses on the future development of generative probabilistic modeling and invites four front-line experts and scholars in generative artificial intelligence to share the cutting-edge progress of generative modeling and discuss how to build a multi-modal unified generative modeling method and other important future issues. direction.

Forum Agenda

Forum Chairman

## Li Chongxuan, Associate Professor, Hillhouse School of Artificial Intelligence, Renmin University of China

Li Chongxuan is an associate professor and doctoral supervisor at Hillhouse School of Artificial Intelligence, Renmin University of China. He received his bachelor's and doctorate degrees from Tsinghua University from 2010 to 2019. Mainly researching machine learning and deep generative models, representative work is deployed in the text-to-image large model DALL·E 2, Stable Diffusion, and the text-to-video large model ViDu. He has won the International Conference ICLR Best Paper Award, Wu Wenjun Outstanding Youth Award, Wu Wenjun Artificial Intelligence First Prize, and China Computer Federation Outstanding Doctoral Thesis. Selected into the Boxin Plan and Beijing Science and Technology Star, he has presided over and participated in many projects of the National Natural Science Foundation and the Ministry of Science and Technology. Served as chairman of international conferences such as ICLR and NeurIPS.

#Chen Jianfei, associate professor at Tsinghua University

Chen Jianfei received his bachelor's degree and PhD degree in computer science from Tsinghua University in 2014 and 2019 respectively, and collaborated with Professor Zhu Jun in the TSAIL group. His research interests include efficient machine learning, especially quantized neural networks, stochastic optimization algorithms, and probabilistic inference algorithms. In the past, he has also developed several scalable topic model training systems. In 2019, Chen Jianfei won the CCF Outstanding Doctoral Thesis Award for his outstanding work. He also won the gold medal in the China Informatics Olympiad in 2009. In 2018, Chen Jianfei co-founded RealAI, a notable achievement in his career.

Speech theme and guest introduction

(in order of speaking)

##1,

Video generation before report along progress Introduction: Different from image generation, video generation faces huge challenges in terms of content consistency, long video generation, and computing resource consumption. However, video generation still achieved rapid development in 2023, with the emergence of excellent models such as Stable Video Diffusion, Runway Gen-2, Video Diffusion Transformer, and Sora. This report first introduces the current challenges faced by video generation, then introduces in detail the latest excellent video generation models, and finally provides an outlook on the technological development of video generation.

#Lu Zhiwu, Professor at Renmin University of China

Dr. Lu Zhiwu is a professor and doctoral supervisor at the Hillhouse School of Artificial Intelligence, Renmin University of China. In 2005, he graduated from the Department of Information Science, School of Mathematical Sciences, Peking University, with a Master of Science degree; in 2011, he graduated from the Department of Computer Science, City University of Hong Kong, with a PhD degree. His research direction is machine learning and computer vision. Design the first Chinese universal multi-modal pre-training model Wenlan BriVL. Published the first Nature sub-journal paper in the field of multimodality. Earlier than OpenAI released Sora-like video generation base VDT.

2、Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Introduction to the report: The speaker will introduce the latest visual generation framework Visual AutoRegressive Modeling, which implements Next Scale Prediction based on Visual tokenizer combined with transformer, making GPT style possible for the first time Autoregressive visual generation surpasses Diffusion in terms of effect, speed, and scaling capabilities, and ushered in Scaling Laws in the field of visual generation. This sharing will introduce you to the classic diffusion model and the Auto Regressive model that has recently attracted everyone's attention. Cutting edge progress.

Jiang Yi, Bytedance GenAI researcher

##Jiang Yi, Bytedance GenAI researcher, Graduated from Zhejiang University, his representative works include Sparse R-CNN, ByteTrack, UNINEXT, etc. His current research interests are mainly in the research and development of computer vision and visual generation basic models. He has published more than 30 papers in conferences and journals such as CVPR, ICCV, NeurIPS, ICLR, ICML, ECCV, etc., many of which have been accepted into Oral, Spotlight, His work is open sourced on github and has accumulated 20K stars.

3、Several issues in visual generation

Report Introduction: In recent years, visual generative models have achieved breakthrough progress in the field of artificial intelligence, attracting widespread attention in the industry. However, with the development of technology, key issues that need to be solved in this field have become increasingly prominent, calling on researchers to invest more energy in in-depth discussions. This report aims to sort out and summarize several important issues facing this field, and also share the author's preliminary thoughts and insights on the following topics: 1. The ultimate pursuit of exploring generative models; 2. The problem of visual signal splitting; 3. The dilemma of Tokenizer ; 4. The inherent conflict problem of the diffusion model; 5. Whether the diffusion model is a maximum likelihood estimate. The report hopes that these discussions will attract the attention of the academic community and contribute to promoting continued innovation and development in this field.

##Gu Shuyang, researcher in the Visual Computing Group of Microsoft Research Asia

Gu Shuyang received his bachelor's degree and PhD degree from the Department of Automation, University of Science and Technology of China in 2017 and 2022 respectively. He is currently a researcher in the Visual Computing Group of Microsoft Research Asia, focusing on research Direction for generative models in computer vision. Research interests mainly include the theory and application of generative adversarial networks and diffusion models, exploring a new generation of generative models, and quality assessment of generative models. He has published many papers at CVPR, ICCV, ECCV and other conferences and served as a reviewer for many conferences and journals.

Personal homepage: https://cientgu.github.io/

##4、

Large model Efficient parallel reasoning methodReport Introduction:

AIGC large model has achieved widespread application results, but its inefficient sequential reasoning process often leads to poor user experience and high costs Deployment costs. This report will introduce how to improve the inference efficiency of large models from the perspective of inference algorithms, and explore acceleration methods in other aspects such as model architecture, sequence compression, and cache optimization.

##Deng Zhijie,

Shanghai Jiao Tong University Assistant Professor, Qingyuan Research Institute

Deng Zhijie is an assistant professor and doctoral supervisor at Qingyuan Research Institute, School of Electrical Engineering, Shanghai Jiao Tong University. The main research directions are generative models and machine learning. He has published more than 20 papers as the first/corresponding author in conferences and journals such as ICML, NeurIPS, ICLR, and CVPR. Won the NVIDIA Pioneer Research Award. The research work is supported by the National Natural Science Foundation of China, Shanghai Science and Technology Innovation Action Plan, CCF-Baichuan-Inbo Large Model Fund and other projects.

5、Round table discussion

Round table discussion guest:

##Chen Jianfei丨Associate Professor of Tsinghua University (Moderator)

Lu Zhiwu丨Professor of Renmin University of China

江 yi丨Head of ByteDance GenAI

古纺游丨Researcher of the Visual Computing Group at Microsoft Research Asia

Deng Zhijie丨Assistant Professor at Qingyuan Research Institute of Shanghai Jiao Tong University

李 Chongxuan丨Associate Professor of Renmin University of China

Scan the QR code to register immediately and participate in the conference registration

This conference adopts online Integrating offline and online modes, the registration channel has been opened, welcome to scan the QR code to register for free.

Due to limited offline seats, please complete registration as early as possible. The organizing committee will review according to the registration order and send a review result notification before the meeting. The public session will be broadcast live online to registered users.

The above is the detailed content of 2024 Intelligent Source Conference Agenda Revealed丨Generative Model. For more information, please follow other related articles on the PHP Chinese website!