Search Results for author: Dan Gutfreund

Found 22 papers, 5 papers with code

Learning from Invalid Data: On Constraint Satisfaction in Generative Models

no code implementations27 Jun 2023 Giorgio Giannone, Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed

This is particularly problematic when the generated data must satisfy constraints, for example, to meet product specifications in engineering design or to adhere to the laws of physics in a natural scene.

Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design

no code implementations6 Feb 2023 Lyle Regenwetter, Akash Srivastava, Dan Gutfreund, Faez Ahmed

This paper doubles as a review and a practical guide to evaluation metrics for deep generative models (DGMs) in engineering design.

Drug Discovery Learning Theory +1

LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design

1 code implementation30 Aug 2022 Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Faez Ahmed

LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms.


Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind

no code implementations4 Aug 2022 Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka

To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102. 11938) provides a suite of tasks designed to evaluate commonsense reasoning about agents' goals and actions that even young infants exhibit.

Few-Shot Learning Imitation Learning

A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics

no code implementations NeurIPS 2021 Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Josh Tenenbaum, Charles Sutton

In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy.

Bayesian Inference Bilevel Optimization +3

AGENT: A Benchmark for Core Psychological Reasoning

no code implementations24 Feb 2021 Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua B. Tenenbaum, Tomer D. Ullman

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life.

Core Psychological Reasoning

A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics

no code implementations1 Jan 2021 Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Joshua B. Tenenbaum, Charles Sutton

As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.

Bayesian Inference Common Sense Reasoning +2

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling

no code implementations25 Oct 2020 Akash Srivastava, Yamini Bansal, Yukun Ding, Cole Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.



no code implementations ICLR 2020 Akash Srivastava, Yamini Bansal, Yukun Ding, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some latent noise variate.


ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models

no code implementations NeurIPS 2019 Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz

Although we focus on object recognition here, data with controls can be gathered at scale using automated tools throughout machine learning to generate datasets that exercise models in new ways thus providing valuable feedback to researchers.

Ranked #50 on Image Classification on ObjectNet (using extra training data)

BIG-bench Machine Learning Image Classification +1

Reasoning About Human-Object Interactions Through Dual Attention Networks

no code implementations ICCV 2019 Tete Xiao, Quanfu Fan, Dan Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou

The model not only finds when an action is happening and which object is being manipulated, but also identifies which part of the object is being interacted with.

Human-Object Interaction Detection

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