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Prototypical Networks for Few-shot Learning
This work proposes Prototypical Networks for few-shot classification, and provides an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
An attention based model that automatically learns to describe the content of images is introduced that can be trained in a deterministic manner using standard backpropagation techniques and stochastically by maximizing a variational lower bound.
Gated Graph Sequence Neural Networks
This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.
Fairness through awareness
A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
We describe an approach for unsupervised learning of a generic, distributed sentence encoder. Using the continuity of text from books, we train an encoder-decoder model that tries to reconstruct the…
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
- Yukun Zhu, Ryan Kiros, S. Fidler
- Computer ScienceIEEE International Conference on Computer Vision…
- 22 June 2015
To align movies and books, a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book are proposed.
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
This work introduces the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder, and shows that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic.
Learning Fair Representations
We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the…
Meta-Learning for Semi-Supervised Few-Shot Classification
This work proposes novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes, and confirms that these models can learn to improve their predictions due to unlabeling examples, much like a semi-supervised algorithm would.
Generative Moment Matching Networks
This work forms a method that generates an independent sample via a single feedforward pass through a multilayer perceptron, as in the recently proposed generative adversarial networks, using MMD to learn to generate codes that can then be decoded to produce samples.