Publications

On Variational Learning of Controllable Representations for Text without Supervision
Peng Xu
Yanshuai Cao
Jackie Cheung
The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or … (voir plus)extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.
2020-01-01
ICML (publié)
proceedings.mlr.press
openreview.net
You could have said that instead: Improving Chatbots with Natural Language Feedback
Makesh Narsimhan Sreedhar
Kun Ni
Siva Reddy
The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (voir plus)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.
2020-01-01
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains
Florence Baillin
Aline Lefebvre
Amandine Pedoux
Yann Beauxis
Denis-Alexander Engemann
Anna Maruani
Frederique Amsellem
Thomas Bourgeron
Richard Delorme
Guillaume Dumas
2019-12-06
(publié)
doi.org
Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia
Clara A. Moreau
Sebastian Urchs
Pierre Orban
Catherine Schramm
Guillaume Dumas
Aurélie Labbe
Guillaume Huguet
Elise Douard
Pierre-Olivier Quirion
Amy Lin
Leila Kushan
Stephanie Grot
David Luck
Adrianna Mendrek
Stephane Potvin
Emmanuel Stip
Thomas Bourgeron
Alan C. Evans
Carrie E. Bearden
Pierre (Louis) Bellec … (voir 1 de plus)
Sébastien Jacquemont
16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (voir plus)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibited worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.
2019-12-06
bioRxiv (prépublication)
doi.org
Approximate information state for partially observed systems
Jayakumar Subramanian
Aditya Mahajan
The standard approach for modeling partially observed systems is to model them as partially observable Markov decision processes (POMDPs) an… (voir plus)d obtain a dynamic program in terms of a belief state. The belief state formulation works well for planning but is not ideal for online reinforcement learning because the belief state depends on the model and, as such, is not observable when the model is unknown.In this paper, we present an alternative notion of an information state for obtaining a dynamic program in partially observed models. In particular, an information state is a sufficient statistic for the current reward which evolves in a controlled Markov manner. We show that such an information state leads to a dynamic programming decomposition. Then we present a notion of an approximate information state and present an approximate dynamic program based on the approximate information state. Approximate information state is defined in terms of properties that can be estimated using sampled trajectories. Therefore, they provide a constructive method for reinforcement learning in partially observed systems. We present one such construction and show that it performs better than the state of the art for three benchmark models.
2019-12-01
IEEE Conference on Decision and Control (publié)
doi.org
Expressiveness and Learning of Hidden Quantum Markov Models
Sandesh M. Adhikary
Siddarth Srinivasan
Geoff Gordon
Byron Boots
Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (voir plus)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.
2019-12-01
ArXiv (preprint)
arxiv.org
arxiv.org
Forgetting at biologically realistic levels of neurogenesis in a large-scale hippocampal model
Lina M. Tran
Sheena A. Josselyn
Blake Richards
Paul W. Frankland
2019-12-01
Behavioural Brain Research (publié)
doi.org
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Maxime Gasse
Ambros Gleixner
Andrea Lodi
Felipe Serrano
2019-12-01
ArXiv (preprint)
doi.org
arxiv.org
Networked control of coupled subsystems: Spectral decomposition and low-dimensional solutions
Shuang Gao
Aditya Mahajan
In this paper, we investigate optimal networked control of coupled subsystems where the dynamics and the cost couplings depend on an underly… (voir plus)ing weighted graph. We use the spectral decomposition of the graph adjacency matrix to decompose the overall system into (L+1) systems with decoupled dynamics and cost, where L is the rank of the adjacency matrix. Consequently, the optimal control input at each subsystem can be computed by solving (L+1) decoupled Riccati equations. A salient feature of the result is that the solution complexity depends on the rank of the adjacency matrix rather than the size of the network (i.e., the number of nodes). Therefore, the proposed solution framework provides a scalable method for synthesizing and implementing optimal control laws for large-scale systems.
2019-12-01
IEEE Conference on Decision and Control (publié)
doi.org
Restless bandits with controlled restarts: Indexability and computation of Whittle index
Nima Akbarzadeh
Aditya Mahajan
Motivated by applications in machine repair, queueing, surveillance, and clinic care, we consider a scheduling problem where a decision make… (voir plus)r can reset m out of n Markov processes at each time. Processes that are reset, restart according to a known probability distribution and processes that are not reset, evolve in a Markovian manner. Due to the high complexity of finding an optimal policy, such scheduling problems are often modeled as restless bandits. We show that the model satisfies a technical condition known as indexability. For indexable restless bandits, the Whittle index policy, which computes a function known as Whittle index for each process and resets the m processes with the lowest index, is known to be a good heuristic. The Whittle index is computed by solving an auxiliary Markov decision problem for each arm. When the optimal policy for this auxiliary problem is threshold based, we use ideas from renewal theory to derive closed form expression for the Whittle index. We present detailed numerical experiments which suggest that Whittle index policy performs close to the optimal policy and performs significantly better than myopic policy, which is a commonly used heuristic.
2019-12-01
IEEE Conference on Decision and Control (publié)
doi.org
Deconstructing and reconstructing word embedding algorithms
Edward Daniel Newell
Kian Kenyon-Dean
Jackie Cheung
Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applicati… (voir plus)ons. Given the historical success of word embeddings in NLP, we propose a retrospective on some of the most well-known word embedding algorithms. In this work, we deconstruct Word2vec, GloVe, and others, into a common form, unveiling some of the necessary and sufficient conditions required for making performant word embeddings. We find that each algorithm: (1) fits vector-covector dot products to approximate pointwise mutual information (PMI); and, (2) modulates the loss gradient to balance weak and strong signals. We demonstrate that these two algorithmic features are sufficient conditions to construct a novel word embedding algorithm, Hilbert-MLE. We find that its embeddings obtain equivalent or better performance against other algorithms across 17 intrinsic and extrinsic datasets.
2019-11-29
ArXiv (prépublication)
arxiv.org
arxiv.org
Driver perceptions of advanced driver assistance systems and safety
Sophie Le Page
Jason Millar
Kelly Selina Bronson
Shalaleh Rismani
AJung Moon
Advanced driver assistance systems (ADAS) are often used in the automotive industry to highlight innovative improvements in vehicle safety. … (voir plus)However, today it is unclear whether certain automation (e.g., adaptive cruise control, lane keeping, parking assist) increases safety of our roads. In this paper, we investigate driver awareness, use, perceived safety, knowledge, training, and attitudes toward ADAS with different automation systems/features. Results of our online survey (n=1018) reveal that there is a significant difference in frequency of use and perceived safety for different ADAS features. Furthermore, we find that at least 70% of drivers activate an ADAS feature"most or all of the time"when driving, yet we find that at least 40% of drivers report feeling that ADAS often compromises their safety when activated. We also find that most respondents learn how to use ADAS in their vehicles by trying it out on the road by themselves, rather than through any formal driver education and training. These results may mirror how certain ADAS features are often activated by default resulting in high usage rates. These results also suggest a lack of driver training and education for safely interacting with, and operating, ADAS, such as turning off systems/features. These findings contribute to a critical discussion about the overall safety implications of current ADAS, especially as they enable higher-level automation features to creep into personal vehicles without a lockstep response in training, regulation, and policy.
2019-11-25
ArXiv (prépublication)
arxiv.org
arxiv.org