HoME: a Household Multimodal Environment
Simon Brodeur
Ethan Perez
Ankesh Anand
Florian Golemo
Luca Celotti
Florian Strub
Jean Rouat
Hugo Larochelle
Aaron Courville
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction… (see more) with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.
2018-01-01
ICLR (Workshop) (published)
openreview.net
openreview.net
How can we do better ? Pitfalls in biomedical challenge design and how to address them
Annika Reinke
Matthias Eisenmann
Sinan Onogur
Marko Stankovic
Patrick Scholz
Tal Arbel
Hrvoje Bogunovic
Andrew P. Bradley
Aaron
Carass
Carolin Feldmann
Alejandro F. Frangi
Peter M. Full
Bram Ginneken Van
Ginneken
Allan Hanbury
Katrin Honauer
Michal Kozubek
Adam Bennett
Landman … (see 22 more)
Keno März
Oskar Maier
Klaus Maier-Hein
Bjoern Menze
Henning Müller
Peter F. Neher
Wiro Niessen
NASIR RAJPOOT
Catherine Gregory
Sharp
Korsuk Sirinukunwattana
Stefanie Speidel
Christian Stock
Danail
Stoyanov
Abdel Aziz Taha
F. V. D. Sommen
Ching-Wei Wang
Marc-André Weber
Guoyan Zheng
Pierre Jannin
Lena Maier-Hein
Since the first MICCAI grand challenge was organized in 2007 [1], the impact of biomedical image analysis challenges on both the research fi… (see more)eld as well as on individual careers has been steadily growing. For example, the acceptance of a journal article today often depends on the performance of a new algorithm being assessed against the state-ofthe-art work on publicly available challenge datasets. Furthermore, the results are also important for the individuals scientific careers as well as the potential that algorithms can be translated into clinical practice. Yet, while the publication of papers in scientific journals and prestigious conferences, such as MICCAI, undergoes strict quality control, the design and organization of challenges do not. To investigate the effect of common practice, we have formed an international initiative dedicated to analyzing and improving a variety of aspects related to biomedical challenge design, execution and reporting [2]. In the first part of our abstract presentation at LABELS workshop, we are going to present some of the major pitfalls related to biomedical image analysis challenges today. Specifically, we will look at the following research questions: RQ1: How robust are challenge rankings? What is the effect of – the specific test cases used? – the specific metric variant(s) applied? – the rank aggregation method chosen (e.g. aggregation of metric values with the mean vs median)? ? Shared first/senior authors.
Image-to-image translation for cross-domain disentanglement
Abel Gonzalez-Garcia
Joost van de Weijer
Yoshua Bengio
Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data dis… (see more)tribution. There has also been increased interest in disentangling the internal representations learned by deep methods to further improve their performance and achieve a finer control. In this paper, we bridge these two objectives and introduce the concept of cross-domain disentanglement. We aim to separate the internal representation into three parts. The shared part contains information for both domains. The exclusive parts, on the other hand, contain only factors of variation that are particular to each domain. We achieve this through bidirectional image translation based on Generative Adversarial Networks and cross-domain autoencoders, a novel network component. Our model offers multiple advantages. We can output diverse samples covering multiple modes of the distributions of both domains, perform domain-specific image transfer and interpolation, and cross-domain retrieval without the need of labeled data, only paired images. We compare our model to the state-of-the-art in multi-modal image translation and achieve better results for translation on challenging datasets as well as for cross-domain retrieval on realistic datasets.
arxiv.org
Improving Explorability in Variational Inference with Annealed Variational Objectives
Chin-Wei Huang
Shawn Tan
Alexandre Lacoste
Aaron Courville
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can s… (see more)till limit the density that is ultimately learned. We demonstrate the drawbacks of biasing the true posterior to be unimodal, and introduce Annealed Variational Objectives (AVO) into the training of hierarchical variational methods. Inspired by Annealed Importance Sampling, the proposed method facilitates learning by incorporating energy tempering into the optimization objective. In our experiments, we demonstrate our method's robustness to deterministic warm up, and the benefits of encouraging exploration in the latent space.
arxiv.org
Investigating the viability of Generative Models for Novelty Detection
Vidhi Jain
Aaron Courville
Abstract
LATTER M INIMA WITH SGD
Stanisław Jastrzębski
Zac Kenton
Devansh Arpit
Nicolas Ballas
Asja Fischer
Yoshua Bengio
Amos Storkey
It has been discussed that over-parameterized deep neural networks (DNNs) trained using stochastic gradient descent (SGD) with smaller batch… (see more) sizes generalize better compared with those trained with larger batch sizes. Additionally, model parameters found by small batch size SGD tend to be in flatter regions. We extend these empirical observations and experimentally show that both large learning rate and small batch size contribute towards SGD finding flatter minima that generalize well. Conversely, we find that small learning rates and large batch sizes lead to sharper minima that correlate with poor generalization in DNNs.
2018-01-01
(published)
www.semanticscholar.org
LATTER M INIMA WITH SGD
Stanisław Jastrzębski
Zac Kenton
Devansh Arpit
Nicolas Ballas
Asja Fischer
Yoshua Bengio
Amos Storkey
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
Sandeep Subramanian
Adam Trischler
Yoshua Bengio
Chris Pal
A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on l… (see more)arge amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.
2018-01-01
ICLR (Poster) (published)
openreview.net
openreview.net
Learning Graph Weighted Models on Pictures
Philip Amortila
Guillaume Rabusseau
Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrar… (see more)y families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its edges. In this paper, we consider the problem of learning GWMs defined over the graph family of pictures (or 2-dimensional words). As a proof of concept, we consider regression and classification tasks over the simple Bars & Stripes and Shifting Bits picture languages and provide an experimental study investigating whether these languages can be learned in the form of a GWM from positive and negative examples using gradient-based methods. Our results suggest that this is indeed possible and that investigating the use of gradient-based methods to learn picture series and functions computed by GWMs over other families of graphs could be a fruitful direction.
2018-01-01
ICGI (published)
proceedings.mlr.press
arxiv.org
MetaGAN: An Adversarial Approach to Few-Shot Learning
Ruixiang ZHANG
Tong Che
Zoubin Ghahramani
Yoshua Bengio
Yangqiu Song
In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems. Most state-of-the-art f… (see more)ew-shot classification models can be integrated with MetaGAN in a principled and straightforward way. By introducing an adversarial generator conditioned on tasks, we augment vanilla few-shot classification models with the ability to discriminate between real and fake data. We argue that this GAN-based approach can help few-shot classifiers to learn sharper decision boundary, which could generalize better. We show that with our MetaGAN framework, we can extend supervised few-shot learning models to naturally cope with unlabeled data. Different from previous work in semi-supervised few-shot learning, our algorithms can deal with semi-supervision at both sample-level and task-level. We give theoretical justifications of the strength of MetaGAN, and validate the effectiveness of MetaGAN on challenging few-shot image classification benchmarks.
Nash equilibria for integer programming games
Margarida Carvalho
Andrea Lodi
João Pedro Pedroso
In this paper, we develop algorithmic approaches for a recently defined class of games, the integer programming games. Two general methods t… (see more)o approximate an equilibrium are presented and enhanced in order to improve their practical efficiency. Their performance is analysed through computational experiments in a knapsack game and a competitive lot-sizing game. To the best of our knowledge, this is the first time that equilibria computation methods for general integer programming games are build and computationally tested.
Negative eigenvalues of the Hessian in deep neural networks
Guillaume Alain
Nicolas Le Roux
Pierre-Antoine Manzagol
The loss function of deep networks is known to be non-convex but the precise nature of this nonconvexity is still an active area of research… (see more). In this work, we study the loss landscape of deep networks through the eigendecompositions of their Hessian matrix. In particular, we examine how important the negative eigenvalues are and the benefits one can observe in handling them appropriately.
2018-01-01
ICLR (Workshop) (published)
openreview.net
openreview.net