Search Results for author: Richard S. Zemel

Found 24 papers, 14 papers with code

Wandering Within a World: Online Contextualized Few-Shot Learning

1 code implementation ICLR 2021 Mengye Ren, Michael L. Iuzzolino, Michael C. Mozer, Richard S. Zemel

We aim to bridge the gap between typical human and machine-learning environments by extending the standard framework of few-shot learning to an online, continual setting.

Few-Shot Learning

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

1 code implementation ICLR 2019 Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution.

Node Classification

Incremental Few-Shot Learning with Attention Attractor Networks

1 code implementation NeurIPS 2019 Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel

This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples.

Few-Shot Learning General Classification

Meta-Learning for Semi-Supervised Few-Shot Classification

9 code implementations ICLR 2018 Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.

General Classification Meta-Learning

Deep Spectral Clustering Learning

no code implementations ICML 2017 Marc T. Law, Raquel Urtasun, Richard S. Zemel

We derive a closed-form expression for the gradient that is efficient to compute: the complexity to compute the gradient is linear in the size of the training mini-batch and quadratic in the representation dimensionality.

Clustering Metric Learning

Prototypical Networks for Few-shot Learning

42 code implementations NeurIPS 2017 Jake Snell, Kevin Swersky, Richard S. Zemel

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

Few-Shot Image Classification General Classification +3

End-to-End Instance Segmentation with Recurrent Attention

1 code implementation CVPR 2017 Mengye Ren, Richard S. Zemel

While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene.

Autonomous Driving Image Captioning +7

Learning to Generate Images with Perceptual Similarity Metrics

1 code implementation19 Nov 2015 Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel

We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM).

Image Classification Image Generation +3

Skip-Thought Vectors

16 code implementations NeurIPS 2015 Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler

The end result is an off-the-shelf encoder that can produce highly generic sentence representations that are robust and perform well in practice.

Decoder Sentence

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

3 code implementations10 Nov 2014 Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel

Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space.

Decoder Language Modelling +3

A Multiplicative Model for Learning Distributed Text-Based Attribute Representations

no code implementations NeurIPS 2014 Ryan Kiros, Richard S. Zemel, Ruslan Salakhutdinov

In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings.

Attribute Authorship Attribution +9

Input Warping for Bayesian Optimization of Non-stationary Functions

1 code implementation5 Feb 2014 Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions.

Bayesian Optimization Gaussian Processes

Cardinality Restricted Boltzmann Machines

no code implementations NeurIPS 2012 Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan R. Salakhutdinov, Ryan P. Adams

The Restricted Boltzmann Machine (RBM) is a popular density model that is also good for extracting features.

Efficient Sampling for Bipartite Matching Problems

no code implementations NeurIPS 2012 Maksims Volkovs, Richard S. Zemel

Bipartite matching problems characterize many situations, ranging from ranking in information retrieval to correspondence in vision.

Information Retrieval Retrieval

Collaborative Ranking With 17 Parameters

no code implementations NeurIPS 2012 Maksims Volkovs, Richard S. Zemel

The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking.

Collaborative Ranking

Collaborative Filtering and the Missing at Random Assumption

1 code implementation20 Jun 2012 Benjamin Marlin, Richard S. Zemel, Sam Roweis, Malcolm Slaney

Rating prediction is an important application, and a popular research topic in collaborative filtering.

Collaborative Filtering

Ranking via Sinkhorn Propagation

no code implementations9 Jun 2011 Ryan Prescott Adams, Richard S. Zemel

It is of increasing importance to develop learning methods for ranking.

Information Retrieval Retrieval

Learning Hybrid Models for Image Annotation with Partially Labeled Data

no code implementations NeurIPS 2008 Xuming He, Richard S. Zemel

Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain.

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