Author Archives: Qi Wang

Miles Cranmer’s Talk

Hi, everyone! We have a guest speaker for our Seminar, and you are all cordially invited to the AMLab Seminar on Thursday 3rd December at 16:00 CET on Zoom, where‪ Miles Cranmer will give a talk titled “LAGRANGIAN NEURAL NETWORKS”.

Title : LAGRANGIAN NEURAL NETWORKS

Abstract : Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary lagrangian using neural networks. In contrast to models that learn Hamiltonians, LNNs do not require canonical coordinates and thus perform well in situations where canonical momenta are unknown or difficult to compute. Unlike previous approaches, our method does not restrict the functional form of learned energies and will produce energy-conserving models for a variety of tasks. We test our approach on a double pendulum and a relativistic particle, demonstrating energy conservation where a baseline approach incurs dissipation and modeling relativity without canonical coordinates where a Hamiltonianapproach fails. Finally, we show how this model can be applied to graphs and continuous systems using a Lagrangian Graph Network, and demonstrate it on the1D wave equation.

Paper Link: https://arxiv.org/pdf/2003.04630.pdf

To gain more deep insights into LAGRANGIAN NEURAL NETWORKS, feel free to join and discuss it! See you there!

David Duvenaud’s Talk

Hi, everyone! We have a guest speaker for our Seminar, and you are all cordially invited to the AMLab Seminar on Tuesday 24th November at 16:00 CET on Zoom, where‪ David Duvenaud will give a talk titled “Latent Stochastic Differential Equations for Irregularly-Sampled Time Series”.

Title: Latent Stochastic Differential Equations for Irregularly-Sampled Time Series

Abstract: Much real-world data is sampled at irregular intervals, but most time series models require regularly-sampled data. Continuous-time models address this problem, but until now only deterministic (ODE) models or linear-Gaussian models were efficiently trainable with millions of parameters. We construct a scalable algorithm for computing gradients of samples from stochastic differential equations (SDEs), and for gradient-based stochastic variational inference in function space, all with the use of adaptive black-box SDE solvers. This allows us to fit a new family of richly-parameterized distributions over time series. We apply latent SDEs to motion capture data, and to construct infinitely-deep Bayesian neural networks.

The technical details are in this paper: https://arxiv.org/abs/2001.01328 And the code is available at: https://github.com/google-research/torchsde

To gain more deep insights into neural stochastic differential equations, feel free to join and discuss it! See you there!

Bio: David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.

Leon Lang’s Talk

Hi everyone, You are all cordially invited to the AMLab Seminar on Thursday 12nd Nov. at 16:00 CET on Zoom, where‪ Leon Lang will give a talk titled “A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels“.

Title: A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels

Abstract: Group equivariant convolutional networks (GCNNs) endow classical convolutional networks with additional symmetry priors, which can lead to a considerably improved performance. Recent advances in the theoretical description of GCNNs revealed that such models can generally be understood as performing convolutions with G-steerable kernels, that is, kernels that satisfy an equivariance constraint themselves. While the G-steerability constraint has been derived, it has to date only been solved for specific use cases – a general characterization of Gsteerable kernel spaces is still missing. This work provides such a characterization for the practically relevant case of G being any compact group. Our investigation is motivated by a striking analogy between the constraints underlying steerable kernels on the one hand and spherical tensor operators from quantum mechanics on the other hand. By generalizing the famous Wigner-Eckart theorem for spherical tensor operators, we prove that steerable kernel spaces are fully understood and parameterized in terms of 1) generalized reduced matrix elements, 2) ClebschGordan coefficients, and 3) harmonic basis functions on homogeneous spaces.

Link to paper: https://arxiv.org/pdf/2010.10952.pdf

To gain more deep insights into Group Equivariance, feel free to join and discuss it 🙂 !

Tim Bakker’s Talk

Hi everyone,

You are all cordially invited to the AMLab Seminar on Thursday 5th November at 4:00 p.m CET on Zoom, where‪ Tim Bakker will give a talk titled ” Experimental design for MRI by greedy policy search “.

Title: Experimental design for MRI by greedy policy search

Abstract: In today’s clinical practice, magnetic resonance imaging (MRI) is routinely accelerated through subsampling of the associated Fourier domain. Currently, the construction of these subsampling strategies – known as experimental design – relies primarily on heuristics. We propose to learn experimental design strategies for accelerated MRI with policy gradient methods. Unexpectedly, our experiments show that a simple greedy approximation of the objective leads to solutions nearly on-par with the more general non-greedy approach. We offer a partial explanation for this phenomenon rooted in greater variance in the non-greedy objective’s gradient estimates, and experimentally verify that this variance hampers non-greedy models in adapting their policies to individual MR images. We empirically show that this adaptivity is key to improving subsampling designs.

Paper Link: https://arxiv.org/pdf/2010.16262.pdf

To gain more deep insights into MRI research using Reinforcement Learning, feel free to join and discuss it! See you there 🙂 !

Andy Keller’s Talk

Hi everyone, You are all cordially invited to the AMLab Seminar on Thursday 29th October at 2:00 p.m CET on Zoom, where‪ Andy Keller will give a talk titled “Self Normalizing Flows “. (Note that the time slot for this talk is modified a bit, 2 hours advanced than previous ones, it will be appreciated if you can save this in your calendar.)

Title: Self Normalizing Flows

Abstract: Efficient gradient computation of the Jacobian determinant term is a core problem of the normalizing flow framework. Thus, most proposed flow models either restrict to a function class with easy evaluation of the Jacobian determinant, or an efficient estimator thereof. However, these restrictions limit the performance of such density models, frequently requiring significant depth to reach desired performance levels. In this work, we propose Self Normalizing Flows, a flexible framework for training normalizing flows by replacing expensive terms in the gradient by learned approximate inverses at each layer. This reduces the computational complexity of each layer’s exact update from O(D^3) to O(D^2), allowing for the training of flow architectures which were otherwise computationally infeasible, while also providing efficient sampling. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts, while surpassing the performance of their functionally constrained counterparts.

To gain more deep insights into this recently developed normalizing flow, feel free to join and discuss it 🙂 !

Talk by Wouter Kool

Hey, guys~ You are all cordially invited to the AMLab Seminar on Thursday 15th October at 16:00 CEST on Zoom, where‪ Wouter Kool will give a talk titled “Gumbel Mathemagic“.

Title: Gumbel Mathemagic

Abstract: Those who have seen the talk “Stochastic Beams and Where to Find Them” (https://www.facebook.com/icml.imls/videos/895968107420746/) can tune in 20 mins late as I will explain to you the mathemagic behind Stochastic Beam Search, an extension of the Gumbel-Max trick that enables sampling sequences without replacement. After that I will discuss Ancestral-Gumbel-Top-k Sampling, which is a generalization of Stochastic Beam Search. Finally, I will derive a multi-sample REINFORCE estimator with built-in baseline, based on sampling without replacement. All made possible by the humble Gumbel! 🙂 Bring your own snacks!

To gain more deep insights into Gumbel tricks and how to stabilize gradient estimates, feel free to join and discuss it!

Talk by Eric Nalisnick

Hi everyone, you are all cordially invited to the AMLab Seminar on Thursday 8th October at 16:00 CEST on Zoom, where‪ Eric Nalisnick will give a talk titled ” Specifying Priors on Predictive Complexity “.

Title: Specifying Priors on Predictive Complexity

Abstract: Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Priors that seem benign and uninformative can have unintuitive and detrimental effects on a model’s predictions. For this reason, we propose predictive complexity priors: a functional prior that is defined by comparing the model’s predictions to those of a reference function. Although originally defined on the model outputs, we transfer the prior to the model parameters via a change of variables. The traditional Bayesian workflow can then proceed as usual. We apply our predictive complexity prior to modern machine learning tasks such as reasoning over neural network depth and sharing of statistical strength for few-shot learning.

Link to paper : https://arxiv.org/abs/2006.10801 To gain more deep insights into priors in Bayesian models, feel free to join and discuss it!

Talk by Nutan Chen

Hi everyone, we have a guest speaker Nutan Chen from ARGMAX.AI and you are all cordially invited to the AMLab Seminar on Thursday 1st October at 16:00 CEST on Zoom, where‪ Nutan will give a talk titled ” Distance in Latent Space “.

Title : Distance in Latent Space

Abstract : Measuring the similarity between data points often requires domain knowledge. It can in parts be compensated by relying on unsupervised methods such as latent-variable models, where the similarity/distance is estimated in a more compact latent space. However, deep generative models such as vanilla VAEs are not distance-preserving. Therefore, this type of model is unreliable for tasks such as precise distance measurement or smooth interpolation directly from the latent space. To solve this problem, we proposed novel methods based VAEs to constrain or measure the distance in the latent space.

In the first section of this talk, I will explore a method that embeds dynamic movement primitives into the latent space of a time-dependent VAE framework (deep variational Bayes filters). Experimental results show that our framework generalizes well, e.g., switches between movements or changing goals. Additionally, the distance between two data points that are close in time is constrained, which results in influencing the data structure of the hidden space. In the second section, I will show how we transferred ideas from Riemannian geometry to deep generative models, letting the distance between two points be the shortest path on a Riemannian manifold induced by the transformation. The method yields a principled distance measure, provides a tool for visual inspection of deep generative models, and an alternative to linear interpolation in latent space. In the third section, I will propose an extension to the framework of VAEs that allows learning flat latent manifolds, where the Euclidean metric is a proxy for the similarity between data points. This is achieved by defining the latent space as a Riemannian manifold and by regularizing the metric tensor to be a scaled identity matrix. This results in a computational efficient distance metric which is practical for applications in real-time scenarios.

Paper Link : Learning flat manifold of VAEs. In International Conference on Machine Learning (ICML). 2020.

To gain more deep insights into connections between VAEs and manifolds and see how these are applied to robotics, feel free to join and discuss it!

Talk by Abubakar Abid

Hi, guys~ We have a guest speaker Abubakar Abid and you are all cordially invited to the AMLab Seminar on Thursday 17th September at 16:00 CEST on Zoom, where‪ Abubakar will give a talk titled ” Interactive UIs for Your Machine Learning Models “.

Title: Interactive UIs for Your Machine Learning Models

Abstract: Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model development and to gain trust in ML. The accessibility challenge also makes collaboration more difficult and limits the ML researcher’s exposure to realistic data and scenarios that occur in the wild. To improve accessibility and facilitate collaboration, we developed an open-source Python package, Gradio, which allows researchers to rapidly generate a visual interface for their ML models. Gradio makes accessing any ML model as easy as opening a URL in your browser. Our development of Gradio is informed by interviews with a number of machine learning researchers who participate in interdisciplinary collaborations. We developed these features and carried out a case study to understand Gradio’s usefulness and usability in the setting of a machine learning collaboration between a researcher and a cardiologist.

To gain more deep insights into understanding your machine learning models, feel free to join and discuss it! See you there!

Talk by Elise van der Pol

Hi everyone,

You are all cordially invited to the AMLab Seminar on Thursday 10th September at 16:00 CEST on Zoom, where‪ Elise van der Pol will give a talk titled “MDP Homomorphic Networks for Deep Reinforcement Learning “.

Paper link: https://arxiv.org/pdf/2006.16908.pdf and https://arxiv.org/pdf/2002.11963.pdf

Title: MDP Homomorphic Networks for Deep Reinforcement Learning

Abstract: This talk discusses MDP homomorphic networks for deep reinforcement learning. MDP homomorphic networks are neural networks that are equivariant under symmetries in the joint state-action space of an MDP. Current approaches to deep reinforcement learning do not usually exploit knowledge about such structure. By building this prior knowledge into policy and value networks using an equivariance constraint, we can reduce the size of the solution space. We specifically focus on group-structured symmetries (invertible transformations). Additionally, we introduce an easy method for constructing equivariant network layers numerically, so the system designer need not solve the constraints by hand, as is typically done.

We construct MDP homomorphic MLPs and CNNs that are equivariant under either a group of reflections or rotations. We show that such networks converge faster than unstructured baselines on CartPole, a grid world and Pong.

To gain more deep insights on Deep Reinforcement Learning, feel free to join it and discuss! See you there!