Search Results for author: Miguel Angel Bautista

Found 16 papers, 5 papers with code

Generating Molecular Conformer Fields

no code implementations27 Nov 2023 Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly, Joshua M. Susskind, Miguel Angel Bautista

In this paper we tackle the problem of generating conformers of a molecule in 3D space given its molecular graph.

Adaptivity and Modularity for Efficient Generalization Over Task Complexity

no code implementations13 Oct 2023 Samira Abnar, Omid Saremi, Laurent Dinh, Shantel Wilson, Miguel Angel Bautista, Chen Huang, Vimal Thilak, Etai Littwin, Jiatao Gu, Josh Susskind, Samy Bengio

We investigate how the use of a mechanism for adaptive and modular computation in transformers facilitates the learning of tasks that demand generalization over the number of sequential computation steps (i. e., the depth of the computation graph).


Is Generalized Dynamic Novel View Synthesis from Monocular Videos Possible Today?

no code implementations12 Oct 2023 Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M. Susskind, Alexander G. Schwing

In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.

Novel View Synthesis Test

Value function estimation using conditional diffusion models for control

no code implementations9 Jun 2023 Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista, Devon Hjelm, Alexander Toshev, Josh Susskind

A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data.

Continuous Control

Manifold Diffusion Fields

no code implementations24 May 2023 Ahmed A. Elhag, Joshua M. Susskind, Miguel Angel Bautista

Our approach allows to sample continuous functions on manifolds and is invariant with respect to rigid and isometric transformations of the manifold.

f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation

no code implementations10 Oct 2022 Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Angel Bautista, Josh Susskind

In this work, we propose f-DM, a generalized family of DMs which allows progressive signal transformation.

Image Generation

GAUDI: A Neural Architect for Immersive 3D Scene Generation

1 code implementation27 Jul 2022 Miguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Josh Susskind

We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera.

Image Generation Scene Generation

FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction

1 code implementation CVPR 2022 Zhenpei Yang, Zhile Ren, Miguel Angel Bautista, Zaiwei Zhang, Qi Shan, QiXing Huang

In this paper, we present FvOR, a learning-based object reconstruction method that predicts accurate 3D models given a few images with noisy input poses.

Object Reconstruction Pose Estimation

Fast and Explicit Neural View Synthesis

no code implementations12 Jul 2021 Pengsheng Guo, Miguel Angel Bautista, Alex Colburn, Liang Yang, Daniel Ulbricht, Joshua M. Susskind, Qi Shan

We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.

Novel View Synthesis

Unconstrained Scene Generation with Locally Conditioned Radiance Fields

1 code implementation ICCV 2021 Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W. Taylor, Joshua M. Susskind

In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose scenes into a collection of many local radiance fields that can be rendered from a free moving camera.

Scene Generation

Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

2 code implementations ICCV 2021 Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, Joshua M. Susskind

To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.

Multi-Task Learning Scene Understanding +1

On the generalization of learning-based 3D reconstruction

no code implementations27 Jun 2020 Miguel Angel Bautista, Walter Talbott, Shuangfei Zhai, Nitish Srivastava, Joshua M. Susskind

State-of-the-art learning-based monocular 3D reconstruction methods learn priors over object categories on the training set, and as a result struggle to achieve reasonable generalization to object categories unseen during training.

3D Reconstruction

Equivariant Neural Rendering

1 code implementation ICML 2020 Emilien Dupont, Miguel Angel Bautista, Alex Colburn, Aditya Sankar, Carlos Guestrin, Josh Susskind, Qi Shan

We propose a framework for learning neural scene representations directly from images, without 3D supervision.

Neural Rendering

Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

no code implementations15 May 2019 Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Angel Bautista, Shih-Yu Sun, Carlos Guestrin, Josh Susskind

In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric.

General Classification Meta-Learning +2

Error-Correcting Factorization

no code implementations27 Feb 2015 Miguel Angel Bautista, Oriol Pujol, Fernando de la Torre, Sergio Escalera

To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes.

Multi-class Classification

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