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Hamiltonian Neural Networks
S. Greydanus, M. Dzamba, J. Yosinski
Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we draw inspiration from Hamiltonian mechanics to train models that learn and respect exact conservation laws in an unsupervised manner. […] [PDF]
Conference on Neural Information Processing Systems (NeurIPS), 2019
Uplift Modeling for Multiple Treatments with Cost Optimization
Z. Zhao, T. Harinen
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. […] [PDF]
IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019
Multi-Task Multi-Sensor Fusion for 3D Object Detection
M. Liang, B. Yang, Y. Chen, R. Hu, R. Urtasun
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. […] [PDF]
Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Automatic Discovery of the Statistical Types of Variables in a Dataset
I. Valera, Z. Ghahramani
A common practice in statistics and machine learning is to assume that the statistical data types (e.g., ordinal, categorical or real-valued) of variables, and usually also the likelihood model, is known. However, as the availability of real-world data increases, this assumption becomes too restrictive. […] [PDF]
International Conference on Machine Learning (ICML), 2017
General Latent Feature Modeling for Data Exploration Tasks
I. Valera, M. Pradier, Z. Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. […] [PDF]
ICML Workshop on Human Interpretability in Machine Learning (ICML), 2017
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