Publications

BabyAI 1.1
David Y. T. Hui
Maxime Chevalier-Boisvert
Dzmitry Bahdanau
Yoshua Bengio
The BabyAI platform is designed to measure the sample efficiency of training an agent to follow grounded-language instructions. BabyAI 1.0 p… (see more)resents baseline results of an agent trained by deep imitation or reinforcement learning. BabyAI 1.1 improves the agent's architecture in three minor ways. This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90.4 %. We hope that these improvements increase the computational efficiency of BabyAI experiments and help users design better agents.
2020-12-31
(published)
arxiv.org
arxiv.org
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 … (see more)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.
2020-12-31
ICLR (published)
openreview.net
openreview.net
CAMAP: Artificial neural networks unveil the role of 1 codon arrangement in modulating MHC-I peptides 2 presentation discovery of minor histocompatibility with
Tariq Daouda
Maude Dumont-Lagacé
Albert Feghaly
Yahya Benslimane
6. Rébecca
Panes
Mathieu Courcelles
Mohamed Benhammadi
Lea Harrington
Pierre Thibault
François Major
Yoshua Bengio
Étienne Gagnon
S. Lemieux
Claude Perreault
30 MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and 31 neoplastic cells by CD8 T cells. However… (see more), accurately predicting the MAP repertoire remains 32 difficult, because only a fraction of the transcriptome generates MAPs. In this study, we 33 investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We 34 developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), 35 predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons 36 (MCCs), while excluding the MCC per se . CAMAP predictions were significantly more accurate 37 when using original codon sequences than shuffled codon sequences which reflect amino acid 38 usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity 39 to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, 40 transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction 41 accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions 42 flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation 43 of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon 44 arrangement in the regulation of MAP presentation and support integration of both translational 45 and post-translational events in predictive algorithms to ameliorate modeling of the 46 immunopeptidome. 47 48 49 they modulated the levels of SIINFEKL presentation in both constructs, but enhanced translation efficiency could only be detected for OVA-RP. These data show that codon arrangement can modulate MAP presentation strength without any changes in the amino
2020-12-31
(published)
www.semanticscholar.org
CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation
Tariq Daouda
Maude Dumont-Lagacé
Albert Feghaly
Yahya Benslimane
Rebecca Panes
Mathieu Courcelles
Mohamed Benhammadi
Lea Harrington
Pierre Thibault
François Major
Yoshua Bengio
Étienne Gagnon
Sébastien Lemieux
Claude Perreault
MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune syst… (see more)em to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using anin-vitroassay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.
2020-12-31
PLoS Computational Biology (published)
doi.org
CAMAP: Artificial neural networks unveil the role of 1 codon arrangement in modulating MHC-I peptides 2 presentation
Tariq Daouda
Maude Dumont-Lagacé
Albert Feghaly
Yahya Benslimane
6. Rébecca
Panes
Mathieu Courcelles
Mohamed Benhammadi
Lea Harrington
Pierre Thibault
François Major
Yoshua Bengio
Étienne Gagnon
S. Lemieux
Claude Perreault
30 MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and 31 neoplastic cells by CD8 T cells. However… (see more), accurately predicting the MAP repertoire remains 32 difficult, because only a fraction of the transcriptome generates MAPs. In this study, we 33 investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We 34 developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), 35 predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons 36 (MCCs), while excluding the MCC per se . CAMAP predictions were significantly more accurate 37 when using original codon sequences than shuffled codon sequences which reflect amino acid 38 usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity 39 to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, 40 transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction 41 accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions 42 flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation 43 of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon 44 arrangement in the regulation of MAP presentation and support integration of both translational 45 and post-translational events in predictive algorithms to ameliorate modeling of the 46 immunopeptidome. 47 48 49 they modulated the levels of SIINFEKL presentation in both constructs, but enhanced translation efficiency could only be detected for OVA-RP. These data show that codon arrangement can modulate MAP presentation strength without any changes in the amino
2020-12-31
(published)
www.semanticscholar.org
Can Open Source Licenses Help Regulate Lethal Autonomous Weapons?
Cheng Lin
AJung Moon
Lethal autonomous weapon systems (LAWS, ethal autonomous weapon also known as killer robots) are a real and emerging technology that have th… (see more)e potential to radically transform warfare. Because of the myriad of moral, legal, privacy, and security risks the technology introduces, many scholars and advocates have called for a ban on the development, production, and use of fully autonomous weapons [1], [2].
2020-12-31
IEEE technology & society magazine (published)
doi.org
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
Dinghuai Zhang
Kartik Ahuja
Yilun Xu
Yisen Wang
Aaron Courville
Can models with particular structure avoid being biased towards spurious correlation in out-of-distribution (OOD) generalization? Peters et … (see more)al. (2016) provides a positive answer for linear cases. In this paper, we use a functional modular probing method to analyze deep model structures under OOD setting. We demonstrate that even in biased models (which focus on spurious correlation) there still exist unbiased functional subnetworks. Furthermore, we articulate and demonstrate the functional lottery ticket hypothesis: full network contains a subnetwork that can achieve better OOD performance. We then propose Modular Risk Minimization to solve the subnetwork selection problem. Our algorithm learns the subnetwork structure from a given dataset, and can be combined with any other OOD regularization methods. Experiments on various OOD generalization tasks corroborate the effectiveness of our method.
2020-12-31
ICML (published)
doi.org
proceedings.mlr.press
Capacity Expansion in the College Admission Problem
Federico Bobbio
Margarida Carvalho
Andrea Lodi
Alfredo Torrico
2020-12-31
arXiv.org (preprint)
dblp.uni-trier.de
ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models
Salva Rühling Cachay
Venkatesh Ramesh
Jason N. S. Cole
Howard Barker
David Rolnick
Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in repla… (see more)cing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking. To fill this gap, we build a large dataset, ClimART, with more than 10 million samples from present, pre-industrial, and future climate conditions, based on the Canadian Earth System Model. ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of datasets and network architectures used in prior work.
2020-12-31
NeurIPS Datasets and Benchmarks (published)
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… (see more)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.
2020-12-31
ICLR (published)
openreview.net
openreview.net
A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
Shuyuan Zhang
Mingde Zhao
Zhen Liu
Sitao Luan
Shuyuan Zhang
Doina Precup
Yoshua Bengio
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during plan… (see more)ning. The agent uses a bottleneck mechanism over a set-based representation to force the number of entities to which the agent attends at each planning step to be small. In experiments, we investigate the bottleneck mechanism with several sets of customized environments featuring different challenges. We consistently observe that the design allows the planning agents to generalize their learned task-solving abilities in compatible unseen environments by attending to the relevant objects, leading to better out-of-distribution generalization performance.
2020-12-31
Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (published)
doi.org
openreview.net
Consistency and Rate of Convergence of Switched Least Squares System Identification for Autonomous Switched Linear Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we investigate the problem of system identification for autonomous switched linear systems with complete state observations.… (see more) We propose switched least squares method for the identification for switched linear systems, show that this method is strongly consistent, and derive data-dependent and data-independent rates of convergence. In particular, our data-dependent rate of convergence shows that, almost surely, the system identification error is O (cid:0)(cid:112) log( T ) /T (cid:1) where T is the time horizon. These results show that our method for switched linear systems has the same rate of convergence as least squares method for non-switched linear systems. We compare our results with those in the literature. We present numerical examples to illustrate the performance of the proposed system identification method.
2020-12-31
arXiv.org (preprint)
dblp.uni-trier.de