Search Results for author: Laurens van der Maaten

Found 51 papers, 33 papers with code

Guarantees of confidentiality via Hammersley-Chapman-Robbins bounds

1 code implementation3 Apr 2024 Kamalika Chaudhuri, Chuan Guo, Laurens van der Maaten, Saeed Mahloujifar, Mark Tygert

The HCR bounds appear to be insufficient on their own to guarantee confidentiality of the inputs to inference with standard deep neural nets, "ResNet-18" and "Swin-T," pre-trained on the data set, "ImageNet-1000," which contains 1000 classes.

Image Classification

Bounding Training Data Reconstruction in Private (Deep) Learning

1 code implementation28 Jan 2022 Chuan Guo, Brian Karrer, Kamalika Chaudhuri, Laurens van der Maaten

Differential privacy is widely accepted as the de facto method for preventing data leakage in ML, and conventional wisdom suggests that it offers strong protection against privacy attacks.

A Systematic Study of Bias Amplification

1 code implementation27 Jan 2022 Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones, Aaron Adcock

To enable this study, we design a simple image-classification problem in which we can tightly control (synthetic) biases.

BIG-bench Machine Learning Image Classification

Omnivore: A Single Model for Many Visual Modalities

2 code implementations CVPR 2022 Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra

Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data.

 Ranked #1 on Scene Recognition on SUN-RGBD (using extra training data)

Action Classification Action Recognition +3

Submix: Practical Private Prediction for Large-Scale Language Models

no code implementations4 Jan 2022 Antonio Ginart, Laurens van der Maaten, James Zou, Chuan Guo

Recent data-extraction attacks have exposed that language models can memorize some training samples verbatim.

Language Modelling

CrypTen: Secure Multi-Party Computation Meets Machine Learning

1 code implementation NeurIPS 2021 Brian Knott, Shobha Venkataraman, Awni Hannun, Shubho Sengupta, Mark Ibrahim, Laurens van der Maaten

To foster adoption of secure MPC in machine learning, we present CrypTen: a software framework that exposes popular secure MPC primitives via abstractions that are common in modern machine-learning frameworks, such as tensor computations, automatic differentiation, and modular neural networks.

BIG-bench Machine Learning Image Classification +4

Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems

1 code implementation NeurIPS 2021 Ruihan Wu, Chuan Guo, Awni Hannun, Laurens van der Maaten

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, analyze natural language, infer preferences, rank options, etc.

BIG-bench Machine Learning Object Detection +1

Measuring Data Leakage in Machine-Learning Models with Fisher Information

1 code implementation23 Feb 2021 Awni Hannun, Chuan Guo, Laurens van der Maaten

This information leaks either through the model itself or through predictions made by the model.

BIG-bench Machine Learning

Physical Reasoning Using Dynamics-Aware Models

1 code implementation20 Feb 2021 Eltayeb Ahmed, Anton Bakhtin, Laurens van der Maaten, Rohit Girdhar

A common approach to solving physical reasoning tasks is to train a value learner on example tasks.

Visual Reasoning

Making Paper Reviewing Robust to Bid Manipulation Attacks

1 code implementation9 Feb 2021 Ruihan Wu, Chuan Guo, Felix Wu, Rahul Kidambi, Laurens van der Maaten, Kilian Q. Weinberger

We develop a novel approach for paper bidding and assignment that is much more robust against such attacks.

Data Appraisal Without Data Sharing

no code implementations11 Dec 2020 Mimee Xu, Laurens van der Maaten, Awni Hannun

We show that in private, forward influence functions provide an appealing trade-off between high quality appraisal and required computation, in spite of label noise, class imbalance, and missing data.

The Trade-Offs of Private Prediction

1 code implementation9 Jul 2020 Laurens van der Maaten, Awni Hannun

This is problematic when the training data needs to remain private.

Forward Prediction for Physical Reasoning

1 code implementation18 Jun 2020 Rohit Girdhar, Laura Gustafson, Aaron Adcock, Laurens van der Maaten

Physical reasoning requires forward prediction: the ability to forecast what will happen next given some initial world state.

Visual Reasoning

Secure multiparty computations in floating-point arithmetic

no code implementations9 Jan 2020 Chuan Guo, Awni Hannun, Brian Knott, Laurens van der Maaten, Mark Tygert, Ruiyu Zhu

Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares).

Mathematical Proofs Privacy Preserving +1

Convolutional Networks with Dense Connectivity

no code implementations8 Jan 2020 Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

Object Recognition

Measuring Dataset Granularity

1 code implementation21 Dec 2019 Yin Cui, Zeqi Gu, Dhruv Mahajan, Laurens van der Maaten, Serge Belongie, Ser-Nam Lim

We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.

Clustering Few-Shot Learning

Self-Supervised Learning of Pretext-Invariant Representations

7 code implementations CVPR 2020 Ishan Misra, Laurens van der Maaten

The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images.

Contrastive Learning object-detection +5

Privacy-Preserving Multi-Party Contextual Bandits

no code implementations11 Oct 2019 Awni Hannun, Brian Knott, Shubho Sengupta, Laurens van der Maaten

This paper considers a learning setting in which multiple parties aim to train a contextual bandit together in a private way: the parties aim to maximize the total reward but do not want to share any of the relevant information they possess with the other parties.

Multi-Armed Bandits Privacy Preserving

PHYRE: A New Benchmark for Physical Reasoning

2 code implementations NeurIPS 2019 Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick

The benchmark is designed to encourage the development of learning algorithms that are sample-efficient and generalize well across puzzles.

Visual Reasoning

Does Object Recognition Work for Everyone?

no code implementations6 Jun 2019 Terrance DeVries, Ishan Misra, Changhan Wang, Laurens van der Maaten

The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset.

Object Object Recognition

Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search

no code implementations CVPR 2019 Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz, Yixuan Li, Dhruv Mahajan

Empirical evaluations of this defense strategy on ImageNet suggest that it is very effective in attack settings in which the adversary does not have access to the image database.

Evaluating Text-to-Image Matching using Binary Image Selection (BISON)

no code implementations19 Jan 2019 Hexiang Hu, Ishan Misra, Laurens van der Maaten

Providing systems the ability to relate linguistic and visual content is one of the hallmarks of computer vision.

Image Captioning Image Retrieval +1

Anytime Stereo Image Depth Estimation on Mobile Devices

3 code implementations26 Oct 2018 Yan Wang, Zihang Lai, Gao Huang, Brian H. Wang, Laurens van der Maaten, Mark Campbell, Kilian Q. Weinberger

Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints.

Stereo Depth Estimation

Learning by Asking Questions

no code implementations CVPR 2018 Ishan Misra, Ross Girshick, Rob Fergus, Martial Hebert, Abhinav Gupta, Laurens van der Maaten

We also show that our model asks questions that generalize to state-of-the-art VQA models and to novel test time distributions.

Question Answering Visual Question Answering

Separating Self-Expression and Visual Content in Hashtag Supervision

1 code implementation CVPR 2018 Andreas Veit, Maximilian Nickel, Serge Belongie, Laurens van der Maaten

The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models.


Countering Adversarial Images using Input Transformations

1 code implementation ICLR 2018 Chuan Guo, Mayank Rana, Moustapha Cisse, Laurens van der Maaten

This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.

Adversarial Defense General Classification +1

Memory-Efficient Implementation of DenseNets

6 code implementations21 Jul 2017 Geoff Pleiss, Danlu Chen, Gao Huang, Tongcheng Li, Laurens van der Maaten, Kilian Q. Weinberger

A 264-layer DenseNet (73M parameters), which previously would have been infeasible to train, can now be trained on a single workstation with 8 NVIDIA Tesla M40 GPUs.

Submanifold Sparse Convolutional Networks

7 code implementations5 Jun 2017 Benjamin Graham, Laurens van der Maaten

Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.

3D Part Segmentation

Inferring and Executing Programs for Visual Reasoning

5 code implementations ICCV 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.

Visual Question Answering (VQA) Visual Reasoning

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

5 code implementations CVPR 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.

Question Answering Visual Question Answering +1

Densely Connected Convolutional Networks

144 code implementations CVPR 2017 Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.

Breast Tumour Classification Crowd Counting +8

Revisiting Visual Question Answering Baselines

3 code implementations27 Jun 2016 Allan Jabri, Armand Joulin, Laurens van der Maaten

Visual question answering (VQA) is an interesting learning setting for evaluating the abilities and shortcomings of current systems for image understanding.

Binary Classification Multiple-choice +2

Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance

no code implementations25 Mar 2016 Kevin van Hecke, Guido de Croon, Laurens van der Maaten, Daniel Hennes, Dario Izzo

We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision.


Modeling Time Series Similarity with Siamese Recurrent Networks

no code implementations15 Mar 2016 Wenjie Pei, David M. J. Tax, Laurens van der Maaten

Traditional techniques for measuring similarities between time series are based on handcrafted similarity measures, whereas more recent learning-based approaches cannot exploit external supervision.

domain classification General Classification +5

Approximated and User Steerable tSNE for Progressive Visual Analytics

no code implementations5 Dec 2015 Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, Anna Vilanova

Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results.

Dimensionality Reduction

Learning Visual Features from Large Weakly Supervised Data

no code implementations6 Nov 2015 Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache

We train convolutional networks on a dataset of 100 million Flickr photos and captions, and show that these networks produce features that perform well in a range of vision problems.

Representation Learning Word Similarity

Time Series Classification using the Hidden-Unit Logistic Model

no code implementations16 Jun 2015 Wenjie Pei, Hamdi Dibeklioğlu, David M. J. Tax, Laurens van der Maaten

We present a new model for time series classification, called the hidden-unit logistic model, that uses binary stochastic hidden units to model latent structure in the data.

Action Recognition Action Unit Detection +9

Speeding Up Tracking by Ignoring Features

no code implementations CVPR 2014 Lu Zhang, Hamdi Dibeklioglu, Laurens van der Maaten

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features.


Marginalizing Corrupted Features

no code implementations27 Feb 2014 Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Weinberger

In this paper, we propose a third, alternative approach to combat overfitting: we extend the training set with infinitely many artificial training examples that are obtained by corrupting the original training data.

Bayesian Inference

Structure Preserving Object Tracking

no code implementations CVPR 2013 Lu Zhang, Laurens van der Maaten

We also show that SPOT can improve the performance of single-object trackers by simultaneously tracking different parts of the object.

Multi-Object Tracking Object


5 code implementations15 Jan 2013 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 normally runs in O(N^2).

Visualizing Data using t-SNE

1 code implementation JMLR 2008 Laurens van der Maaten, Geoffrey Hinton

The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.

Dimensionality Reduction

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