9.7k citations · 26.5k across the 43 of their papers we have counts for
11 papers · 1 filter
On Robustness in Multimodal Learning
Brandon McKinzie, Joseph Cheng, Vaishaal Shankar +3
Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned with understanding how models…
Soft Calibration Objectives for Neural Networks
Archit Karandikar, Nicholas Cain, Dustin Tran +4
Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-con…
Mitigating Bias in Calibration Error Estimation
Rebecca Roelofs, Nicholas Cain, Jonathon Shlens +1
For an AI system to be reliable, the confidence it expresses in its decisions must match its accuracy. To assess the degree of match, examples are typically binned by confidence an…
Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens +2
State-of-the art vision models can achieve superhuman performance on image classification tasks when testing and training data come from the same distribution. However, when models…
A Fourier Perspective on Model Robustness in Computer Vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens +2
Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision. Data augmentation is a commonly used approach for improving robustness, howe…
Accelerating Training of Deep Neural Networks with a Standardization Loss
Jasmine Collins, Johannes Balle, Jonathon Shlens
A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better…
Adversarial Autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly +2
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform…
Net2Net: Accelerating Learning via Knowledge Transfer
Tianqi Chen, Ian Goodfellow, Jonathon Shlens
We introduce techniques for rapidly transferring the information stored in one neural net into another neural net. The main purpose is to accelerate the training of a significantly…
A Tutorial on Independent Component Analysis
Jonathon Shlens
Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides…
A Tutorial on Principal Component Analysis
Jonathon Shlens
Principal component analysis (PCA) is a mainstay of modern data analysis - a black box that is widely used but (sometimes) poorly understood. The goal of this paper is to dispel th…
Zero-Shot Learning by Convex Combination of Semantic Embeddings
Mohammad Norouzi, Tomas Mikolov, Samy Bengio +5
Several recent publications have proposed methods for mapping images into continuous semantic embedding spaces. In some cases the embedding space is trained jointly with the image…