Search Results for author: Chris Pal

Found 38 papers, 16 papers with code

ArK: Augmented Reality with Knowledge Interactive Emergent Ability

no code implementations1 May 2023 Qiuyuan Huang, Jae Sung Park, Abhinav Gupta, Paul Bennett, Ran Gong, Subhojit Som, Baolin Peng, Owais Khan Mohammed, Chris Pal, Yejin Choi, Jianfeng Gao

In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e. g. GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in the physical or virtual world.

Mixed Reality Scene Generation +1

A General Purpose Neural Architecture for Geospatial Systems

no code implementations4 Nov 2022 Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.

Disaster Response Humanitarian +1

Implicit Offline Reinforcement Learning via Supervised Learning

no code implementations21 Oct 2022 Alexandre Piche, Rafael Pardinas, David Vazquez, Igor Mordatch, Chris Pal

Despite the benefits of using implicit models to learn robotic skills via BC, offline RL via Supervised Learning algorithms have been limited to explicit models.

Offline RL reinforcement-learning +1

Neural Attentive Circuits

no code implementations14 Oct 2022 Nasim Rahaman, Martin Weiss, Francesco Locatello, Chris Pal, Yoshua Bengio, Bernhard Schölkopf, Li Erran Li, Nicolas Ballas

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modalities.

Point Cloud Classification text-classification +1

Workflow Discovery from Dialogues in the Low Data Regime

1 code implementation24 May 2022 Amine El Hattami, Stefania Raimondo, Issam Laradji, David Vazquez, Pau Rodriguez, Chris Pal

We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance.

Workflow Discovery

Learned Image Compression for Machine Perception

no code implementations3 Nov 2021 Felipe Codevilla, Jean Gabriel Simard, Ross Goroshin, Chris Pal

Compression that ensures high accuracy on computer vision tasks such as image segmentation, classification, and detection therefore has the potential for significant impact across a wide variety of settings.

Image Compression Image Reconstruction +3

Learning Multi-Objective Curricula for Robotic Policy Learning

1 code implementation6 Oct 2021 Jikun Kang, Miao Liu, Abhinav Gupta, Chris Pal, Xue Liu, Jie Fu

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).

Reinforcement Learning (RL)

Accounting for Variance in Machine Learning Benchmarks

no code implementations1 Mar 2021 Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

Benchmarking BIG-bench Machine Learning +1

Predicting Infectiousness for Proactive Contact Tracing

1 code implementation ICLR 2021 Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT).

DuoRAT: Towards Simpler Text-to-SQL Models

1 code implementation NAACL 2021 Torsten Scholak, Raymond Li, Dzmitry Bahdanau, Harm de Vries, Chris Pal

Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases.


Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track

no code implementations29 Jun 2020 Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne, Maxime Descoteaux, Herve Lombaert, Chris Pal

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.

BIG-bench Machine Learning

Role-Wise Data Augmentation for Knowledge Distillation

1 code implementation ICLR 2020 Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong

To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.

Data Augmentation Knowledge Distillation

Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments

1 code implementation29 Oct 2019 Martin Weiss, Simon Chamorro, Roger Girgis, Margaux Luck, Samira E. Kahou, Joseph P. Cohen, Derek Nowrouzezahrai, Doina Precup, Florian Golemo, Chris Pal

In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task.

Navigate Reinforcement Learning (RL)

Learning Neural Causal Models from Unknown Interventions

2 code implementations2 Oct 2019 Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Bernhard Schölkopf, Michael C. Mozer, Chris Pal, Yoshua Bengio

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.


Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

1 code implementation29 May 2019 Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, Jiancheng Lv

We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer.

Disentanglement Style Transfer +2

Probabilistic Planning with Sequential Monte Carlo methods

no code implementations ICLR 2019 Alexandre Piche, Valentin Thomas, Cyril Ibrahim, Yoshua Bengio, Chris Pal

In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories.

Continuous Control

Towards Standardization of Data Licenses: The Montreal Data License

no code implementations21 Mar 2019 Misha Benjamin, Paul Gagnon, Negar Rostamzadeh, Chris Pal, Yoshua Bengio, Alex Shee

This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning.

Towards Text Generation with Adversarially Learned Neural Outlines

no code implementations NeurIPS 2018 Sandeep Subramanian, Sai Rajeswar Mudumba, Alessandro Sordoni, Adam Trischler, Aaron C. Courville, Chris Pal

We generate outlines with an adversarial model trained to approximate the distribution of sentences in a latent space induced by general-purpose sentence encoders.

Text Generation

Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding

no code implementations NeurIPS 2018 Nan Rosemary Ke, Anirudh Goyal Alias Parth Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio

We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.

Temporal Sequences

Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding

no code implementations11 Sep 2018 Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Michael C. Mozer, Chris Pal, Yoshua Bengio

We consider the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.

Temporal Sequences

Fashion-Gen: The Generative Fashion Dataset and Challenge

3 code implementations21 Jun 2018 Negar Rostamzadeh, Seyedarian Hosseini, Thomas Boquet, Wojciech Stokowiec, Ying Zhang, Christian Jauvin, Chris Pal

We introduce a new dataset of 293, 008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.

Image Generation

ACtuAL: Actor-Critic Under Adversarial Learning

no code implementations13 Nov 2017 Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R. Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio

This makes it fundamentally difficult to train GANs with discrete data, as generation in this case typically involves a non-differentiable function.

Language Modelling

Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks

no code implementations ICLR 2018 Nan Rosemary Ke, Anirudh Goyal, Olexa Bilaniuk, Jonathan Binas, Laurent Charlin, Chris Pal, Yoshua Bengio

A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation.

On orthogonality and learning RNNs with long term dependencies

no code implementations ICML 2017 Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.

Liver lesion segmentation informed by joint liver segmentation

no code implementations24 Jul 2017 Eugene Vorontsov, An Tang, Chris Pal, Samuel Kadoury

We propose a model for the joint segmentation of the liver and liver lesions in computed tomography (CT) volumes.

Computed Tomography (CT) Lesion Detection +3

Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation

no code implementations16 Feb 2017 Michal Drozdzal, Gabriel Chartrand, Eugene Vorontsov, Lisa Di Jorio, An Tang, Adriana Romero, Yoshua Bengio, Chris Pal, Samuel Kadoury

Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods.

Image Segmentation Medical Image Segmentation +2

On orthogonality and learning recurrent networks with long term dependencies

1 code implementation31 Jan 2017 Eugene Vorontsov, Chiheb Trabelsi, Samuel Kadoury, Chris Pal

We find that hard constraints on orthogonality can negatively affect the speed of convergence and model performance.

The Importance of Skip Connections in Biomedical Image Segmentation

1 code implementation14 Aug 2016 Michal Drozdzal, Eugene Vorontsov, Gabriel Chartrand, Samuel Kadoury, Chris Pal

In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation.

Image Segmentation Semantic Segmentation

Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression

no code implementations27 Dec 2015 David Rim, Sina Honari, Md. Kamrul Hasan, Chris Pal

We use a weakly-supervised approach in which identity labels are used to learn the different factors of variation linked to identity separately from factors related to expression.

Emotion Recognition Point Tracking

Delving Deeper into Convolutional Networks for Learning Video Representations

2 code implementations19 Nov 2015 Nicolas Ballas, Li Yao, Chris Pal, Aaron Courville

We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs). Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset.

Action Recognition Temporal Action Localization +1

Brain Tumor Segmentation with Deep Neural Networks

14 code implementations13 May 2015 Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle

Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.

Brain Tumor Segmentation Test +1

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