1.9k citations · 3.7k across the 35 of their papers we have counts for
11 papers · 1 filter
NOMAD Projection
Brandon Duderstadt, Zach Nussbaum, Laurens van der Maaten
The rapid adoption of generative AI has driven an explosion in the size of datasets consumed and produced by AI models. Traditional methods for unstructured data visualization, suc…
A Systematic Study of Bias Amplification
Melissa Hall, Laurens van der Maaten, Laura Gustafson +2
Recent research suggests that predictions made by machine-learning models can amplify biases present in the training data. When a model amplifies bias, it makes certain predictions…
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Ruihan Wu, Chuan Guo, Awni Hannun +1
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech,…
Measuring Data Leakage in Machine-Learning Models with Fisher Information
Awni Hannun, Chuan Guo, Laurens van der Maaten
Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Co…
The Trade-Offs of Private Prediction
Laurens van der Maaten, Awni Hannun
Machine learning models leak information about their training data every time they reveal a prediction. This is problematic when the training data needs to remain private. Private…
Forward Prediction for Physical Reasoning
Rohit Girdhar, Laura Gustafson, Aaron Adcock +1
Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state. We study the performance of state-of-the-art forward-p…
Privacy-Preserving Multi-Party Contextual Bandits
Awni Hannun, Brian Knott, Shubho Sengupta +1
Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total…
PHYRE: A New Benchmark for Physical Reasoning
Anton Bakhtin, Laurens van der Maaten, Justin Johnson +2
Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classic…
Multi-Scale Dense Networks for Resource Efficient Image Classification
Gao Huang, Danlu Chen, Tianhong Li +3
In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network's prediction…
Marginalizing Corrupted Features
Laurens van der Maaten, Minmin Chen, Stephen Tyree +1
The goal of machine learning is to develop predictors that generalize well to test data. Ideally, this is achieved by training on an almost infinitely large training data set that…
Barnes-Hut-SNE
Laurens van der Maaten
The paper presents an O(N log N)-implementation of t-SNE -- an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots and that no…