1 code implementation • 17 May 2024 • Kris Sankaran
Society's capacity for algorithmic problem-solving has never been greater.
1 code implementation • 19 May 2022 • Zhuoyan Xu, Kris Sankaran
We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation.
1 code implementation • 9 Feb 2022 • Adrianna Janik, Kris Sankaran
Among current formulations, concepts defines them by as a direction in a learned representation space.
no code implementations • 9 Feb 2022 • Adrianna Janik, Kris Sankaran
We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection - one of the core use cases of remote sensing in humanitarian applications.
no code implementations • 4 Feb 2022 • Xinran Miao, Kris Sankaran
Models that perform out-of-domain generalization borrow knowledge from heterogeneous source data and apply it to a related but distinct target task.
1 code implementation • 11 Dec 2021 • Minxing Zheng, Xinran Miao, Kris Sankaran
Interpretability has attracted increasing attention in earth observation problems.
no code implementations • 15 Mar 2021 • Adrianna Janik, Jonathan Dodd, Georgiana Ifrim, Kris Sankaran, Kathleen Curran
In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier.
1 code implementation • ICCV 2021 • Yuwei Cheng, Jiannan Zhu, Mengxin Jiang, Jie Fu, Changsong Pang, Peidong Wang, Kris Sankaran, Olawale Onabola, Yimin Liu, Dianbo Liu, Yoshua Bengio
To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.
no code implementations • 1 Jan 2021 • Anthony Ortiz, Kris Sankaran, Olac Fuentes, Christopher Kiekintveld, Pascal Vincent, Yoshua Bengio, Doina Precup
In this work we tackle the problem of out-of-distribution generalization through conditional computation.
1 code implementation • 9 Dec 2020 • Shimaa Baraka, Benjamin Akera, Bibek Aryal, Tenzing Sherpa, Finu Shresta, Anthony Ortiz, Kris Sankaran, Juan Lavista Ferres, Mir Matin, Yoshua Bengio
Glacier mapping is key to ecological monitoring in the hkh region.
2 code implementations • 15 Feb 2020 • Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
1 code implementation • ICLR 2020 • Michel Deudon, Alfredo Kalaitzis, Md Rifat Arefin, Israel Goytom, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
1 code implementation • 17 Dec 2019 • Israel Goytom, Qin Wang, Tianxiang Yu, Kunjie Dai, Kris Sankaran, Xinfei Zhou, Dongdong Lin
Microscopy images are powerful tools and widely used in the majority of research areas, such as biology, chemistry, physics and materials fields by various microscopies (scanning electron microscope (SEM), atomic force microscope (AFM) and the optical microscope, et al.).
no code implementations • 2 Dec 2019 • Jessenia Gonzalez, Debjani Bhowmick, Cesar Beltran, Kris Sankaran, Yoshua Bengio
In this work, we present the application of convolutional neural networks for segmenting water bodies in satellite images.
3 code implementations • 10 Jun 2019 • David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
1 code implementation • 13 May 2019 • Chin-wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville
We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.
no code implementations • 2 May 2019 • Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, Yoshua Bengio
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).