Publications

Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference
Sahand Rezaei-Shoshtari
David Meger
Inna Sharf
Utilization of latent space to capture a lower-dimensional representation of a complex dynamics model is explored in this work. The targeted… (voir plus) application is of a robotic manipulator executing a complex environment interaction task, in particular, cutting a wooden object. We train two flavours of Variational Autoencoders---standard and Vector-Quantised---to learn the latent space which is then used to infer certain properties of the cutting operation, such as whether the robot is cutting or not, as well as, material and geometry of the object being cut. The two VAE models are evaluated with reconstruction, prediction and a combined reconstruction/prediction decoders. The results demonstrate the expressiveness of the latent space for robotic interaction inference and the competitive prediction performance against recurrent neural networks.
2021-01-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
arxiv.org
PresSense: Passive Respiration Sensing via Ambient WiFi Signals in Noisy Environments
Yi Tian Xu
X. T. Chen
Xue Liu
David Meger
Gregory Dudek
Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving u… (voir plus)sers' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection.
2021-01-23
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)
doi.org
Author response: Functional specialization within the inferior parietal lobes across cognitive domains
Ole Numssen
Danilo Bzdok
Gesa Hartwigsen
2021-01-20
(publié)
doi.org
Topological analysis of single-cell data reveals shared glial landscape of macular degeneration and neurodegenerative diseases
Manik Kuchroo
Marcello DiStasio
Eda Calapkulu
Maryam Ige
Le Zhang
Amar H. Sheth
Madhvi Menon
Yu Xing
Scott Gigante
Jessie Huang
Rahul M. Dhodapkar
Bastian Rieck
Guy Wolf
Smita Krishnaswamy
Brian P. Hafler
1 A novel topological machine learning approach applied to single-nucleus RNA sequencing from human retinas… (voir plus) with age-related macular degeneration identifies interacting disease phase-specific glial activation states shared with Alzheimer’s disease and multiple sclerosis. 2 Neurodegeneration occurs in a wide range of diseases, including age-related macular degeneration (AMD), Alzheimer’s disease (AD), and multiple sclerosis (MS), each with distinct inciting events. To determine whether glial transcriptional states are shared across phases of degeneration, we sequenced 50,498 nuclei from the retinas of seven AMD patients and six healthy controls, generating the first single-cell transcriptomic atlas of AMD. We identified groupings of cells implicated in disease pathogenesis by applying a novel topologically-inspired machine learning approach called ‘diffusion condensation.’ By calculating diffusion homology features and performing persistence analysis, diffusion condensation identified activated glial states enriched in the early phases of AMD, AD, and MS as well as an AMD-specific proangiogenic astrocyte state promoting pathogenic neovascularization in advanced AMD. Finally, by mapping the expression of disease-associated genes to glial states, we identified key signaling interactions creating hypotheses for therapeutic intervention. Our topological analysis identified an integrated disease-phase specific glial landscape that is shared across neurodegenerative conditions affecting the central nervous system.
2021-01-19
bioRxiv (prépublication)
doi.org
Correction to: The patient advisor, an organizational resource as a lever for an enhanced oncology patient experience (PAROLEonco): a longitudinal multiple case study protocol
M. P. Pomey
M. de Guise
M. Desforges
K. Bouchard
C. Vialaron
L. Normandin
M. Iliescu-Nelea
I. Fortin
I. Ganache
C. Régis
Z. Rosberger
D. Charpentier
L. Bélanger
M. Dorval
D. P. Ghadiri
M. Lavoie-Tremblay
A. Boivin
J. F. Pelletier
N. Fernandez
A. M. Danino
An amendment to this paper has been published and can be accessed via the original article.
2021-01-13
BMC Health Services Research (publié)
doi.org
Deep learning identifies partially overlapping subnetworks in the human social brain
Hannah Kiesow
R. Nathan Spreng
Avram J. Holmes
Mallar Chakravarty
Andre Marquand
B.T. Thomas Yeo
Danilo Bzdok
2021-01-13
Communications Biology (publié)
doi.org
Systematic detection of brain protein-coding genes under positive selection during primate evolution and their roles in cognition
Guillaume Dumas
Simon Malesys
Thomas Bourgeron
The human brain differs from that of other primates, but the genetic basis of these differences remains unclear. We investigated the evoluti… (voir plus)onary pressures acting on almost all human protein-coding genes (N = 11,667; 1:1 orthologs in primates) based on their divergence from those of early hominins, such as Neanderthals, and non-human primates. We confirm that genes encoding brain-related proteins are among the most strongly conserved protein-coding genes in the human genome. Combining our evolutionary pressure metrics for the protein-coding genome with recent data sets, we found that this conservation applied to genes functionally associated with the synapse and expressed in brain structures such as the prefrontal cortex and the cerebellum. Conversely, several genes presenting signatures commonly associated with positive selection appear as causing brain diseases or conditions, such as micro/macrocephaly, Joubert syndrome, dyslexia, and autism. Among those, a number of DNA damage response genes associated with microcephaly in humans such as BRCA1, NHEJ1, TOP3A, and RNF168 show strong signs of positive selection and might have played a role in human brain size expansion during primate evolution. We also showed that cerebellum granule neurons express a set of genes also presenting signatures of positive selection and that may have contributed to the emergence of fine motor skills and social cognition in humans. This resource is available online and can be used to estimate evolutionary constraints acting on a set of genes and to explore their relative contributions to human traits.
2021-01-12
Genome Research (publié)
doi.org
Adversarial score matching and improved sampling for image generation
Alexia Jolicoeur-Martineau
Rémi Piché-Taillefer
Remi Tachet des Combes
Ioannis Mitliagkas
Denoising Score Matching with Annealed Langevin Sampling (DSM-ALS) has recently found success in generative modeling. The approach works by … (voir plus)first training a neural network to estimate the score of a distribution, and then using Langevin dynamics to sample from the data distribution assumed by the score network. Despite the convincing visual quality of samples, this method appears to perform worse than Generative Adversarial Networks (GANs) under the Fréchet Inception Distance, a standard metric for generative models. We show that this apparent gap vanishes when denoising the final Langevin samples using the score network. In addition, we propose two improvements to DSM-ALS: 1) Consistent Annealed Sampling as a more stable alternative to Annealed Langevin Sampling, and 2) a hybrid training formulation, composed of both Denoising Score Matching and adversarial objectives. By combining these two techniques and exploring different network architectures, we elevate score matching methods and obtain results competitive with state-of-the-art image generation on CIFAR-10.
2021-01-11
ICLR.cc/2021/Conference (poster)
openreview.net
openreview.net
Batch Reinforcement Learning Through Continuation Method
Yijie Guo
Shengyu Feng
Nicolas Roux
Ed Chi
Honglak Lee
Minmin Chen
Many real-world applications of reinforcement learning (RL) require the agent to learn from a fixed set of trajectories, without collecting … (voir plus)new interactions. Policy optimization under this setting is extremely challenging as: 1) the geometry of the objective function is hard to optimize efficiently; 2) the shift of data distributions causes high noise in the value estimation. In this work, we propose a simple yet effective policy iteration approach to batch RL using global optimization techniques known as continuation. By constraining the difference between the learned policy and the behavior policy that generates the fixed trajectories, and continuously relaxing the constraint, our method 1) helps the agent escape local optima; 2) reduces the error in policy evaluation in the optimization procedure. We present results on a variety of control tasks, game environments, and a recommendation task to empirically demonstrate the efficacy of our proposed method.
2021-01-11
ICLR.cc/2021/Conference (poster)
openreview.net
openreview.net
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data
Jonathan Pilault
Amine El hattami
Christopher Pal
Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MT… (voir plus)L must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer, or learning interference. Often, in Natural Language Processing (NLP), a separate model per task is needed to obtain the best performance. However, many fine-tuning approaches are both parameter inefficient, i.e., potentially involving one new model per task, and highly susceptible to losing knowledge acquired during pretraining. We propose a novel Transformer based Hypernetwork Adapter consisting of a new conditional attention mechanism as well as a set of task-conditioned modules that facilitate weight sharing. Through this construction, we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed. We also use a new multi-task data sampling strategy to mitigate the negative effects of data imbalance across tasks. Using this approach, we are able to surpass single task fine-tuning methods while being parameter and data efficient (using around 66% of the data). Compared to other BERT Large methods on GLUE, our 8-task model surpasses other Adapter methods by 2.8% and our 24-task model outperforms by 0.7-1.0% models that use MTL and single task fine-tuning. We show that a larger variant of our single multi-task model approach performs competitively across 26 NLP tasks and yields state-of-the-art results on a number of test and development sets.
2021-01-11
ICLR.cc/2021/Conference (poster)
openreview.net
openreview.net
Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
Rishabh Agarwal
Marlos C. Machado
Pablo Samuel Castro
Bellemare Marc-Emmanuel
Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generali… (voir plus)zation, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite.
2021-01-11
ICLR.cc/2021/Conference (spotlight)
openreview.net
openreview.net
DC3: A learning method for optimization with hard constraints
Priya L. Donti
David Rolnick
J Zico Kolter
Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the… (voir plus) use of deep networks as cheap"approximate solvers."Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility.
2021-01-11
ICLR.cc/2021/Conference (poster)
openreview.net
openreview.net