Publications

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong
Jessie Huang
Guy Wolf
David van Dijk
Smita Krishnaswamy
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly i… (voir plus)n biomedical settings. Recent attempts to model individual trajectories from this data use optimal transport to create pairwise matchings between time points. However, these methods cannot model continuous dynamics and non-linear paths that entities can take in these systems. To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time. Continuous normalizing flows are generally under constrained, as they are allowed to take an arbitrary path from the source to the target distribution. We present TrajectoryNet, which controls the continuous paths taken between distributions to produce dynamic optimal transport. We show how this is particularly applicable for studying cellular dynamics in data from single-cell RNA sequencing (scRNA-seq) technologies, and that TrajectoryNet improves upon recently proposed static optimal transport-based models that can be used for interpolating cellular distributions.
2020-07-13
International Conference on Machine Learning (Accept)
doi.org
proceedings.mlr.press
Attention Option-Critic
Raviteja Chunduru
Doina Precup
2020-07-12
ICML.cc/2020/Workshop/LifelongML (accepté)
openreview.net
openreview.net
A Brief Look at Generalization in Visual Meta-Reinforcement Learning
Safa Alver
Doina Precup
Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training … (voir plus)environments, there have been several studies that investigated the generalization performance of these algorithms. However, there has been no similar study that evaluated the generalization performance of algorithms that were specifically designed for generalization, i.e. meta-reinforcement learning algorithms. In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments. We find that these algorithms can display strong overfitting when they are evaluated on challenging tasks. We also observe that scalability to high-dimensional tasks with sparse rewards remains a significant problem among many of the current meta-reinforcement learning algorithms. With these results, we highlight the need for developing meta-reinforcement learning algorithms that can both generalize and scale.
2020-07-12
ICML.cc/2020/Workshop/LifelongML (accepté)
openreview.net
openreview.net
Chaotic Continual Learning
Touraj Laleh
Mojtaba Faramarzi
Irina Rish
A. Chandar
Training a deep neural network requires the model to go over training data for several epochs and update network parameters. In continual le… (voir plus)arning, this process results in catastrophic forgetting which is one of the core issues of this domain. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to model in a batch incremental setup. This paper proposes a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not updates network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setup. Our experiments on MNIST, CIFAR10, and Omniglot show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning.
2020-07-12
ICML.cc/2020/Workshop/LifelongML (accepté)
openreview.net
openreview.net
Historical Issue Data of Projects on Jira
A. Nicholson
Deeksha M. Arya
Jin L.C. Guo
2020-07-12
(publié)
doi.org
S2RMs: Spatially Structured Recurrent Modules
Nasim Rahaman
Anirudh Goyal
Muhammad Waleed Gondal
Manuel Wuthrich
Stefan Bauer
Y. Sharma
Yoshua Bengio
Bernhard Schölkopf
2020-07-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Towards an Unsupervised Method for Model Selection in Few-Shot Learning
Simon Guiroy
Vikas Verma
Christopher Pal
The study of generalization of neural networks in gradient-based meta-learning has recently great research interest. Previous work on the st… (voir plus)udy of the objective landscapes within the scope of few-shot classification empirically demonstrated that generalization to new tasks might be linked to the average inner product between their respective gradients vectors (Guiroy et al., 2019). Following that work, we study the effect that meta-training has on the learned space of representation of the network. Notably, we demonstrate that the global similarity in the space of representation, measured by the average inner product between the embeddings of meta-test examples, also correlates to generalization. Based on these observations, we propose a novel model-selection criterion for gradient-based meta-learning and experimentally validate its effectiveness.
2020-07-12
ICML.cc/2020/Workshop/LifelongML (accepté)
openreview.net
openreview.net
Vision-Based Goal-Conditioned Policies for Underwater Navigation in the Presence of Obstacles.
Travis Manderson
Juan Camilo Gamboa Higuera
Stefan Wapnick
Jean-François Tremblay
Florian Shkurti
David Meger
Gregory Dudek
We present Nav2Goal, a data-efficient and end-to-end learning method for goal-conditioned visual navigation. Our technique is used to train … (voir plus)a navigation policy that enables a robot to navigate close to sparse geographic waypoints provided by a user without any prior map, all while avoiding obstacles and choosing paths that cover user-informed regions of interest. Our approach is based on recent advances in conditional imitation learning. General-purpose, safe and informative actions are demonstrated by a human expert. The learned policy is subsequently extended to be goal-conditioned by training with hindsight relabelling, guided by the robot's relative localization system, which requires no additional manual annotation. We deployed our method on an underwater vehicle in the open ocean to collect scientifically relevant data of coral reefs, which allowed our robot to operate safely and autonomously, even at very close proximity to the coral. Our field deployments have demonstrated over a kilometer of autonomous visual navigation, where the robot reaches on the order of 40 waypoints, while collecting scientifically relevant data. This is done while travelling within 0.5 m altitude from sensitive corals and exhibiting significant learned agility to overcome turbulent ocean conditions and to actively avoid collisions.
2020-07-11
Robotics: Science and Systems XVI (publié)
doi.org
arxiv.org
Handling Black Swan Events in Deep Learning with Diversely Extrapolated Neural Networks
Maxime Wabartha
Audrey Durand
Vincent François-Lavet
Joelle Pineau
By virtue of their expressive power, neural networks (NNs) are well suited to fitting large, complex datasets, yet they are also known to … (voir plus)produce similar predictions for points outside the training distribution. As such, they are, like humans, under the influence of the Black Swan theory: models tend to be extremely "surprised" by rare events, leading to potentially disastrous consequences, while justifying these same events in hindsight. To avoid this pitfall, we introduce DENN, an ensemble approach building a set of Diversely Extrapolated Neural Networks that fits the training data and is able to generalize more diversely when extrapolating to novel data points. This leads DENN to output highly uncertain predictions for unexpected inputs. We achieve this by adding a diversity term in the loss function used to train the model, computed at specific inputs. We first illustrate the usefulness of the method on a low-dimensional regression problem. Then, we show how the loss can be adapted to tackle anomaly detection during classification, as well as safe imitation learning problems.
2020-07-10
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (publié)
doi.org
On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability (Extended Abstract)
Vincent François-Lavet
Guillaume Rabusseau
Joelle Pineau
Damien Ernst
Raphael Fonteneau
When an agent has limited information on its environment, the suboptimality of an RL algorithm can be decomposed into the sum of two terms: … (voir plus)a term related to an asymptotic bias (suboptimality with unlimited data) and a term due to overfitting (additional suboptimality due to limited data). In the context of reinforcement learning with partial observability, this paper provides an analysis of the tradeoff between these two error sources. In particular, our theoretical analysis formally characterizes how a smaller state representation increases the asymptotic bias while decreasing the risk of overfitting.
2020-07-10
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (publié)
doi.org
SVRG for Policy Evaluation with Fewer Gradient Evaluations
Zilun Peng
Ahmed Touati
Pascal Vincent
Doina Precup
Stochastic variance-reduced gradient (SVRG) is an optimization method originally designed for tackling machine learning problems with a fini… (voir plus)te sum structure. SVRG was later shown to work for policy evaluation, a problem in reinforcement learning in which one aims to estimate the value function of a given policy. SVRG makes use of gradient estimates at two scales. At the slower scale, SVRG computes a full gradient over the whole dataset, which could lead to prohibitive computation costs. In this work, we show that two variants of SVRG for policy evaluation could significantly diminish the number of gradient calculations while preserving a linear convergence speed. More importantly, our theoretical result implies that one does not need to use the entire dataset in every epoch of SVRG when it is applied to policy evaluation with linear function approximation. Our experiments demonstrate large computational savings provided by the proposed methods.
2020-07-10
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Material for IEEE Software paper "How Do Open Source Software Contributors Perceive and Address Usability?"
Wenting Wang
Jinghui Cheng
Jin L.C. Guo
2020-07-09
(publié)
doi.org