No.24-14 Long-Range Meets Scalability: Unveiling a Linear-Time Graph Neural Network for Recommendation at Scale
Recommender systems play a central role in shaping our daily digital experiences, yet achieving both scalability and expressive power...
No.24-13 Contextual Document Embeddings
From Cornell University, Nov 06, 2024
No.24-10 Recreating the Physical Natural World from Images
From Stanford University, Oct 09, 2024All Talks
No.24-14 Long-Range Meets Scalability: Unveiling a Linear-Time Graph Neural Network for Recommendation at Scale
Recommender systems play a central role in shaping our daily digital experiences, yet achieving both scalability and expressive power remains a significant challenge. While Graph Neur...
From Pennsylvania State University, Nov 13, 2024
No.24-13 Contextual Document Embeddings
Dense document embeddings are central to neural retrieval. The dominant paradigm is to train and construct embeddings by running encoders directly on individual documents. In this wor...
From Cornell University, Nov 06, 2024
No.24-12 Graph Neural Networks in Epidemic Modeling: An In-Depth Review and Toolkit
Since the onset of the COVID-19 pandemic, there has been growing interest in epidemic modeling. While traditional mechanistic models effectively describe the mathematical dynamics of ...
From Emory University, Oct 30, 2024
No.24-10 Recreating the Physical Natural World from Images
Today, generative AI models excel at creating visual worlds through pixels, but still often struggle with the comprehension of basic physical concepts such as 3D shape, motion, materi...
From Stanford University, Oct 09, 2024
No.24-09 Human-Computer Conversational Vision-and-Language Navigation
The dynamic realm of Vision-and-Language Navigation (VLN) has garnered significant multidisciplinary interest, resonating within the domains of computer vision, natural language proce...
From University of Adelaide, Oct 08, 2024
No.24-11 Graph Foundation Model in the Era of LLMs
Graph data structures play a crucial role in real life, effectively illustrating the complex relationships and structural dependencies between entities. In recent years, the generaliz...
From University of Hong Kong, Oct 16, 2024
No.24-08 Towards Graph Foundation Model
Graph Foundation Models (GFMs) is a single (neural) model that learns transferable graph representations that can generalize to any new, previously unseen graph. In this talk, we will...
From Michigan State University, Sep 11, 2024
No.24-07 Embracing Changes in Deep Learning: Continual Learning with Augmented and Modularized Memory
Deep learning (DL) has been successful in many applications. However, the conventional DL approaches focus on the end results on fixed datasets/scenarios and fail to handle the dynami...
From The University of New South Wales, Jun 11, 2024
No.24-05 Efficient and Elastic Large Models
Generative LLMs are transforming multiple industries and have proven to be robust for multitude of use cases across industries and settings. One of the key impediments to their widesp...
From Google Research India, May 17, 2024
No.24-06 Generative Sequential Recommendation
In this talk, we first introduce the Sequential Recommendation problem and draw parallels between language modelling and recommender systems. To set the stage, we also briefly cover s...
From University of Glasgow, May 22, 2024