Hamiltonian Graph Networks with ODE Integrators

Abstract: We introduce an approach for imposing physically informed inductive biases in learned simulation models. We combine graph networks with a differentiable ordinary differential equation integrator as a mechanism for predicting future states, and a Hamiltonian as an internal representation. We find that our approach outperforms baselines without these biases in terms of predictive accuracy, energy accuracy, and zero-shot generalization to time-step sizes and integrator orders not experienced during training. This advances the state-of-the-art of learned simulation, and in principle is applicable beyond physical domains.

https://arxiv.org/abs/1909.12790

Hi all,

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I’ve been thinking about this question a lot lately, and wrote my thoughts on it here: https://medium.com/bigdatarepublic/on-machine-learning-team-composition-a9d0d3a3d89

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Would love to hear opposing opinions to sharpen my own.

Hi guys, so intel is selling this usb stick that helps to compute neural networks, has anyone tried it? What is it exactly? Would you recommand it?

Multi-modal learning has traditionally focused on image/video vs text (e.g. image captioning, video description), but does anyone know good datasets for learning between text and time series? Example of time series: stock charts, power plant / wearable sensor readings, music etc.

I am looking for natural language human comments on these types of data. Examples I can think of are:
– stock charts <-> analyst notes
– power plant sensor data <-> operator notes
– wearable sensor data <-> coach notes or commentary
– music data <-> critics or teacher notes

I wonder if there are real-world datasets of these types?

Thanks.

Hi all,

https://github.com/OpenNMT/OpenNMT-tf

Just wanted to share this new major update of OpenNMT-tf, a toolkit for neural machine translation and sequence generation initially released in 2017. It has been completely redesigned for TensorFlow 2.0 and now includes many useful modules and layers that can be reused in other projects, from dataset utilities to beam search decoding.

Fully upgrading to TensorFlow 2.0 required some energy but we feel the changes are positive: the implementation is simpler, more consistent, and more extensible.

Please check it out, and feel free to ask questions!

Hi!

This is our new paper on capsule networks! https://arxiv.org/abs/1908.01300