25 citations · 55 across the 6 of their papers we have counts for
6 papers
Multimodal Conversation Structure Understanding
Kent K. Chang, Mackenzie Hanh Cramer, Anna Ho +3
While multimodal large language models (LLMs) excel at dialogue, whether they can adequately parse the structure of conversation -- conversational roles and threading -- remains un…
Understanding Transformers via N-gram Statistics
Timothy Nguyen
Transformer based large-language models (LLMs) display extreme proficiency with language yet a precise understanding of how they work remains elusive. One way of demystifying trans…
Is forgetting less a good inductive bias for forward transfer?
Jiefeng Chen, Timothy Nguyen, Dilan Gorur +1
One of the main motivations of studying continual learning is that the problem setting allows a model to accrue knowledge from past tasks to learn new tasks more efficiently. Howev…
Architecture Matters in Continual Learning
Seyed Iman Mirzadeh, Arslan Chaudhry, Dong Yin +4
A large body of research in continual learning is devoted to overcoming the catastrophic forgetting of neural networks by designing new algorithms that are robust to the distributi…
Dataset Distillation with Infinitely Wide Convolutional Networks
Timothy Nguyen, Roman Novak, Lechao Xiao +1
The effectiveness of machine learning algorithms arises from being able to extract useful features from large amounts of data. As model and dataset sizes increase, dataset distilla…
Dataset Meta-Learning from Kernel Ridge-Regression
Timothy Nguyen, Zhourong Chen, Jaehoon Lee
One of the most fundamental aspects of any machine learning algorithm is the training data used by the algorithm. We introduce the novel concept of -approximation of datasets, o…