research
∙
09/19/2023
Investigating the Catastrophic Forgetting in Multimodal Large Language Models
Following the success of GPT4, there has been a surge in interest in mul...
share
research
∙
08/30/2023
Emergence of Segmentation with Minimalistic White-Box Transformers
Transformer-like models for vision tasks have recently proven effective ...
research
∙
06/21/2023
Mass-Producing Failures of Multimodal Systems with Language Models
Deployed multimodal systems can fail in ways that evaluators did not ant...
research
∙
06/08/2023
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
The advent of large pre-trained models has brought about a paradigm shif...
research
∙
06/01/2023
White-Box Transformers via Sparse Rate Reduction
In this paper, we contend that the objective of representation learning ...
research
∙
04/08/2023
EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Recently, self-supervised learning (SSL) has achieved tremendous success...
research
∙
02/18/2023
Closed-Loop Transcription via Convolutional Sparse Coding
Autoencoding has achieved great empirical success as a framework for lea...
research
∙
01/04/2023
Unsupervised Manifold Linearizing and Clustering
Clustering data lying close to a union of low-dimensional manifolds, wit...
research
∙
10/30/2022
Unsupervised Learning of Structured Representations via Closed-Loop Transcription
This paper proposes an unsupervised method for learning a unified repres...
research
∙
10/24/2022
Revisiting Sparse Convolutional Model for Visual Recognition
Despite strong empirical performance for image classification, deep neur...
research
∙
02/11/2022
Incremental Learning of Structured Memory via Closed-Loop Transcription
This work proposes a minimal computational model for learning a structur...
research
∙
11/12/2021
