Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

Over recent years, an increasing amount of compute and data has been poured into training
large language models (LLMs), usually by doing one-pass learning on as many tokens as …

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …

Biological and artificial information processing systems form representations that they can
use to categorize, reason, plan, navigate, and make decisions. How can we measure the …

In this paper, we propose a novel data-pruning approach called moving-one-sample-out
(MoSo), which aims to identify and remove the least informative samples from the training …

Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …

Instruction tuning has unlocked powerful capabilities in large language models (LLMs),
effectively using combined datasets to develop generalpurpose chatbots. However, real …

Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size
of the training dataset, particularly in on-device scenarios. However, these scenarios have …

Multimodal large language models acquire their instruction-following capabilities through a
two-stage training process: pre-training on image-text pairs and fine-tuning on supervised …