Search Results for author: Pedro O. Pinheiro

Found 23 papers, 12 papers with code

Structure-based drug design by denoising voxel grids

1 code implementation7 May 2024 Pedro O. Pinheiro, Arian Jamasb, Omar Mahmood, Vishnu Sresht, Saeed Saremi

We present VoxBind, a new score-based generative model for 3D molecules conditioned on protein structures.


Learning to Adapt to Semantic Shift

no code implementations29 Sep 2021 Ryan Y Benmalek, Sabhya Chhabria, Pedro O. Pinheiro, Claire Cardie, Serge Belongie

These models outperform both previous work and static models under both \emph{static} and \emph{continual} semantic shifts, suggesting that ``learning to adapt'' is a useful capability for models and agents in a changing world.

Attribute Meta-Learning

Touch-based Curiosity for Sparse-Reward Tasks

1 code implementation1 Apr 2021 Sai Rajeswar, Cyril Ibrahim, Nitin Surya, Florian Golemo, David Vazquez, Aaron Courville, Pedro O. Pinheiro

Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary in tasks that involve contact-rich motion.

Reinforced active learning for image segmentation

1 code implementation ICLR 2020 Arantxa Casanova, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher J. Pal

Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems.

Active Learning Image Segmentation +3

Neural Multisensory Scene Inference

2 code implementations NeurIPS 2019 Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn

For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e. g., by looking at and touching objects.

Computational Efficiency Representation Learning

Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks

no code implementations1 Jun 2019 Kris Y. Hong, Pedro O. Pinheiro, Scott Weichenthal

Here we present a new method of estimating global variations in outdoor PM$_{2. 5}$ concentrations using satellite images combined with ground-level measurements and deep convolutional neural networks.

Model Selection

Reinforced Imitation Learning from Observations

no code implementations ICLR 2019 Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse.

Imitation Learning

Adaptive Cross-Modal Few-Shot Learning

1 code implementation NeurIPS 2019 Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro

Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested.

Few-Shot Image Classification Few-Shot Learning +1

Where are the Blobs: Counting by Localization with Point Supervision

3 code implementations ECCV 2018 Issam H. Laradji, Negar Rostamzadeh, Pedro O. Pinheiro, David Vazquez, Mark Schmidt

However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.

Object Object Counting +1

Unsupervised Domain Adaptation with Similarity Learning

no code implementations CVPR 2018 Pedro O. Pinheiro

The objective of unsupervised domain adaptation is to leverage features from a labeled source domain and learn a classifier for an unlabeled target domain, with a similar but different data distribution.

General Classification Unsupervised Domain Adaptation

A MultiPath Network for Object Detection

1 code implementation7 Apr 2016 Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár

To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.

Instance Segmentation Object +2

Learning to Refine Object Segments

2 code implementations29 Mar 2016 Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollàr

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

Object Semantic Segmentation

Learning to Segment Object Candidates

2 code implementations NeurIPS 2015 Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar

Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier.

Object object-detection +3

Phrase-based Image Captioning

no code implementations12 Feb 2015 Rémi Lebret, Pedro O. Pinheiro, Ronan Collobert

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.

Descriptive Image Captioning +1

Simple Image Description Generator via a Linear Phrase-Based Approach

no code implementations29 Dec 2014 Remi Lebret, Pedro O. Pinheiro, Ronan Collobert

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.

Descriptive Language Modelling

From Image-level to Pixel-level Labeling with Convolutional Networks

no code implementations CVPR 2015 Pedro O. Pinheiro, Ronan Collobert

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task.

Multiple Instance Learning Object +4

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