908 citations · 2.7k across the 30 of their papers we have counts for
14 papers · 1 filter
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu, Charline Le Lan, Chris J. Maddison +2
The variational auto-encoder (VAE) is a popular method for learning a generative model and embeddings of the data. Many real datasets are hierarchically structured. However, tradit…
Batch Policy Gradient Methods for Improving Neural Conversation Models
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka +2
We study reinforcement learning of chatbots with recurrent neural network architectures when the rewards are noisy and expensive to obtain. For instance, a chatbot used in automate…
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
Liwen Zhang, John Winn, Ryota Tomioka
We propose the Gaussian attention model for content-based neural memory access. With the proposed attention model, a neural network has the additional degree of freedom to control…
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Generative neural samplers are probabilistic models that implement sampling using feedforward neural networks: they take a random input vector and produce a sample from a probabili…
Jointly Learning Multiple Measures of Similarities from Triplet Comparisons
Liwen Zhang, Subhransu Maji, Ryota Tomioka
Similarity between objects is multi-faceted and it can be easier for human annotators to measure it when the focus is on a specific aspect. We consider the problem of mapping objec…
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Ryota Tomioka, Taiji Suzuki
We discuss structured Schatten norms for tensor decomposition that includes two recently proposed norms ("overlapped" and "latent") for convex-optimization-based tensor decompositi…