Search Results for author:
Found 22 papers, 5 papers with code
Learning from Invalid Data: On Constraint Satisfaction in Generative Models
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.
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
This paper doubles as a review and a practical guide to evaluation metrics for deep generative models (DGMs) in engineering design.
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
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
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.
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
The way an object looks and sounds provide complementary reflections of its physical properties.
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics
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.
3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images.
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
To complete the task, an embodied agent must plan a sequence of actions to change the state of a large number of objects in the face of realistic physical constraints.
AGENT: A Benchmark for Core Psychological Reasoning
For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life.
Ranked #1 on
Core Psychological Reasoning
on AGENT
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics
As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.
Template Controllable keywords-to-text Generation
The model takes as input a set of un-ordered keywords, and part-of-speech (POS) based template instructions.
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the latent factors.
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation.
CZ-GEM: A FRAMEWORK FOR DISENTANGLED REPRESENTATION LEARNING
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
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)
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
Then an inference network (encoder)is trained to invert the decoder.
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Reasoning About Human-Object Interactions Through Dual Attention Networks
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.
Examining Interpretable Feature Relationships in Deep Networks for Action recognition
A number of recent methods to understand neural networks have focused on quantifying the role of individual features.
