Publications

Notational Programming for Notebook Environments: A Case Study with Quantum Circuits

Anthony DeArmas

Michael Roberts

Shrutarshi Basu

Tapan Parikh

We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational pro… (voir plus)gramming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from ‘recognition’ of notations to ‘reconfiguration’ of practices and values around programming, and from ‘sketching’ to writing and drawing, or what we call ‘notating.’

2022-10-27

ACM Symposium on User Interface Software and Technology (publié)

doi.org

Deep generative molecular design reshapes drug discovery

Xiangxiang Zeng

Yi Wang

Yuan Luo

Seung-gu Kang

Jian Tang

Felice C. Lightstone

Evandro F. Fang

Wendy Cornell

Ruth Nussinov

Feixiong Cheng

2022-10-26

Cell Reports Medicine (publié)

doi.org

Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?

Enamundram Naga Karthik

Anne Kerbrat

Pierre Labauge

Tobias Granberg

Jason F. Talbott

Daniel S Reich

Massimo Filippi

Rohit Bakshi

Virginie Callot

A. Chandar

Julien Cohen-Adad

Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (voir plus)ears. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast data from 8 different hospitals. Our experiments show that replay is able to achieve positive backward transfer and reduce catastrophic forgetting compared to sequential fine-tuning. Furthermore, replay outperforms the multi-domain training, thereby emerging as a promising solution for the segmentation of MS lesions. The code is available at this link: https://github.com/naga-karthik/continual-learning-ms

2022-10-26

arXiv (prépublication)

doi.org

arxiv.org

Arc travel time and path choice model estimation subsumed

Sobhan Mohammadpour

Emma Frejinger

We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of diﬀerent levels of… (voir plus) granularity. Hitherto these two tasks have been tackled separately under strong assumptions. Using a small example, we illustrate that this can lead to biased results. Results on both real (New York yellow cab) and simulated data show strong performance of our method compared to existing baselines. models and loss functions. It is designed to estimate arc travel time and path choice model parameters simultaneously. We showed that by marginalizing the unobserved variables and using stochastic gradient estimates, we obtain a maximum likelihood estimation even for observations at diﬀerent level of granularity. We showed that we can mix diﬀerent data type when computing the MLE without needing to use a linear combination of losses as

2022-10-24

ArXiv (prépublication)

doi.org

arxiv.org

Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Yuesong Zou

Ahmad Pesaranghader

Ziyang Song

Aman Verma

David L. Buckeridge

Yue Li

The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic wa… (voir plus)y. However, effective extraction of clinical knowledge from the EHR data has been hindered by its sparsity and noisy information. We present GAT-ETM, an end-to-end knowledge graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on EHR reconstruction and drug imputation. GAT-ETM demonstrated superior performance over the alternative methods on both tasks. Moreover, our model learned clinically meaningful graph-informed embedding of the EHR codes. In additional, our model is also able to discover interpretable and accurate patient representations for patient stratification and drug recommendations. Our code is available at Anonymous GitHub.

2022-10-24

Scientific Reports (publié)

doi.org

Invariant representation driven neural classifier for anti-QCD jet tagging

Taoli Cheng

Aaron Courville

We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD si… (voir plus)gnal jets. In establishing the framework for classification-based anomaly detection in jet physics, we demonstrate that, with a \emph{well-calibrated} and \emph{powerful enough feature extractor}, a well-trained \emph{mass-decorrelated} supervised Standard Model neural jet classifier can serve as a strong generic anti-QCD jet tagger for effectively reducing the QCD background. Imposing \emph{data-augmented} mass-invariance (and thus decoupling the dominant factor) not only facilitates background estimation, but also induces more substructure-aware representation learning. We are able to reach excellent tagging efficiencies for all the test signals considered. In the best case, we reach a background rejection rate of 51 and a significance improvement factor of 3.6 at 50 \% signal acceptance, with the jet mass decorrelated. This study indicates that supervised Standard Model jet classifiers have great potential in general new physics searches.

2022-10-23

Journal of High Energy Physics (publié)

doi.org

arxiv.org

Learning Latent Structural Causal Models

Jithendaraa Subramanian

Yashas Annadani

Ivaxi Sheth

Nan Rosemary Ke

Tristan Deleu

Stefan Bauer

Derek Nowrouzezahrai

Samira Ebrahimi Kahou

Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better e… (voir plus)xplanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or high-dimensional vectors. In such settings, the entire Structural Causal Model (SCM) -- structure, parameters, \textit{and} high-level causal variables -- is unobserved and needs to be learnt from low-level data. We treat this problem as Bayesian inference of the latent SCM, given low-level data. For linear Gaussian additive noise SCMs, we present a tractable approximate inference method which performs joint inference over the causal variables, structure and parameters of the latent SCM from random, known interventions. Experiments are performed on synthetic datasets and a causally generated image dataset to demonstrate the efficacy of our approach. We also perform image generation from unseen interventions, thereby verifying out of distribution generalization for the proposed causal model.

2022-10-23

arXiv (prépublication)

doi.org

openreview.net

Machine learning-based incremental learning in interactive domain modelling

Rijul Saini

Gunter Mussbacher

Jin L.C. Guo

Jörg Kienzle

2022-10-23

Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems (publié)

doi.org

Behaviour Learning with Adaptive Motif Discovery and Interacting Multiple Model

Hanging Zhao

Travis Manderson

Hao Zhang

Xue Liu

Gregory Dudek

We propose an approach that enables simultaneous interpretable learning of a high-level discrete behaviour and its low-level rhythmic sub-be… (voir plus)haviour. We do this though a unified reward function, where a reward function that only describes low-level behaviour, with less impact on learning of other behaviours is recovered from few-shot motion demonstrations. To this end, we first extract local behaviour motifs from state-only human demonstrations and random driving samples using an adaptive motif discovery approach derived from the Matrix Profile algorithm. We then optimize parameters for motif discovery by maximizing the sum and entropy over motif sizes. Interacting Multiple Model (IMM) estimators are constructed on top of linear-Gaussian dynamics of discovered motifs, the cumulative distributions over motifs estimated by IMMs serve as the basis of the reward function. By combining the recovered reward with the terrain type signal gathered from the environment, we are able to train a dual-objective off-road vehicle controller that demonstrates both terrain selection and human-like driving behaviours. Compared with related approaches across 10 people, our rhythmic behaviour reward recovery approach enables the controller to produce higher preference over human driving demonstrations. In addition to performing more stable across different people with 87% less variance than the best baseline in rhythmic behaviour indicator, our method reduces the negative effects on higher-level behaviour learning while maintaining high interpretability at all stages of the algorithm.

2022-10-22

IEEE/RJS International Conference on Intelligent Robots and Systems (publié)

doi.org

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

Yandong Ji

Zhongyu Li

Yinan Sun

Xue Bin Peng

Sergey Levine

Glen Berseth

Koushil Sreenath

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Dev… (voir plus)eloping algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the dynamics limitation and motion stability during the control of a dynamic legged robot. Moreover, we need to consider motion planning to shoot the hard-to-model deformable ball rolling on the ground with uncertain friction to a desired location. In this paper, we propose a hierarchical framework that leverages deep reinforcement learning to train (a) a robust motion control policy that can track arbitrary motions and (b) a planning policy to decide the desired kicking motion to shoot a soccer ball to a target. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.

2022-10-22

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

doi.org

arxiv.org

Vehicle Type Specific Waypoint Generation

Yunpeng Liu

Jonathan Wilder Lavington

Adam Ścibior

Frank N. Wood

We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving … (voir plus)behavior. Many foundation behavior models are trained on data that does not include vehicle information, which limits their utility in downstream applications such as planning. Our novel methodology conditionally specializes such a behavior predictive model to a vehicle-type by utilizing byproducts of the reinforcement learning algorithms used to produce vehicle specific controllers. We show how to compose a vehicle specific value function estimate with a generic probabilistic behavior model to generate vehicle-type specific waypoint sequences that are more likely to be physically plausible then their vehicle-agnostic counterparts.

2022-10-22

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publié)

doi.org

arxiv.org

Efficient Queries Transformer Neural Processes

Leo Feng

Hossein Hajimirsadeghi

Yoshua Bengio

Mohamed Osama Ahmed

2022-10-20

NeurIPS.cc/2022/Workshop/MetaLearn (poster)

openreview.net