I am a Principal Researcher at Microsoft Research Amsterdam, where I work on the intersection of deep learning and computational chemistry and physics for molecular simulation. I will discuss how spectral graph theory yields vertex representations and a generalized convolution that shares weights beyond symmetries. This includes the development of new methods for probabilistic graphical models and non-parametric Bayesian models, the development of faster (approximate) inference and learning methods, deep learning, causal inference, reinforcement learning and multi-agent systems and . Opening in September 2021 and in collaboration with researchers in Cambridge, UK, and Beijing, China, the lab will be focused on molecular simulation using machine . Finally, we show preliminary results suggesting that our model yields a nested spatial hierarchy of increasingly abstract categories, analogous to observations from the human ventral temporal cortex. We study the calibration of L2D systems, investigating if the probabilities they output are sound. Informatics Institute Variational autoencoders (VAEs) optimize an objective that comprises a reconstruction loss (the distortion) and a KL term (the rate). My research centers around causal inference and graphical modelling. You are expected to work on fundamental aspects of computer vision by machine learning, deep learning models, and algorithms. Experimental results demonstrate that FANS-RL outperforms existing approaches in terms of return, compactness of the latent state representation, and robustness to varying degrees of non-stationarity. The learning to defer (L2D) framework has the potential to make AI systems safer. Explore 20 Laboratory vacancies in Amsterdam. We compare our model with related supervised approaches, namely the TDANN, and discuss both theoretical and empirical similarities. Both institutes join forces in the development of AI algorithms to improve cancer treatment. In this talk, we show a third way to compute off-policy gradients that exhibit a fair bias/variance tradeoff using a closed-form solution of a proposed non-parametric Bellman equation. AMLAB webpage. We have a guest speaker Laurence Aitchison from the University of Bristol and Laurence will present his research works at our Lab. In these works, we mainly focus on multi-modal scenarios that naturally occur in the real world that depict common concepts, such as image-caption, photo-sketch, video-audio etc. With QUVA Lab, the University of Amsterdam and Qualcomm we are adapting and breaking ground, not only academically but also societally, making Amsterdam an AI center of excellence. Deep learning is a form of machine learning with neural networks, loosely inspired by how neurons process information in the brain. Postbox 94323 He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. A collaboration between CWI, KNAW HuC, KB, Rijksmuseum, Netherlands Institute for Sound and Vision, TNO, the University of Amsterdam, and the VU University of Amsterdam. Through our general framework, we can consider general non-stationary scenarios with different function types and changing frequency, including changes across episodes and within episodes. Selected Publications. Our experiments verify that not only is our system calibrated, but this benefit comes at no cost to accuracy. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts, while training more quickly and surpassing the performance of functionally constrained counterparts. Moreover, it is not even guaranteed to produce valid probabilities due to its parameterization being degenerate for this purpose. Microsoft to open research lab in Amsterdam. A collaboration between Ahold Delhaize and the University of Amsterdam. We support the theoretical analysis with experiments on image classification tasks performed with multi-layer, fully-connected neural networks. Our models accuracy is always comparable (and often superior) to Mozannar & Sontags (2020) models in tasks ranging from hate speech detection to galaxy classification to diagnosis of skin lesions. And then Michal will give a talk titled Learning from graphs: a spectral perspective. What are you going to do. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. Lwe, S.,Madras, D.,Zemel, R.,and Welling, M. Geometric and Physical Quantities improve E (3) Equivariant Message Passing, Brandstetter, Johannes,Hesselink, Rob,Pol, Elise,Bekkers, Erik,and Welling, Max, Self-Supervised Inference in State-Space Models, Detecting dispersed radio transients in real time using convolutional neural networks, Ruhe, David,Kuiack, Mark,Rowlinson, Antonia,Wijers, Ralph,and Forr, Patrick, Pol, Elise,Hoof, Herke,Oliehoek, Frans,and Welling, Max, Deep Policy Dynamic Programming for Vehicle Routing Problems, Kool, Wouter,Hoof, Herke,Gromicho, Joaquim,and Welling, Max, Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation, Whlke, Jan,Schmitt, Felix,and Hoof, Herke, Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods, Hpner, Niklas,Tiddi, Ilaria,and Hoof, Herke, Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders, Keller, T. Anderson,Gao, Qinghe,and Welling, Max, Predictive Coding With Topographic Variational Autoencoders, Topographic VAEs learn Equivariant Capsules, Keller, T. Anderson,Peters, Jorn W.T.,Jaini, Priyank,Hoogeboom, Emiel,Forr, Patrick,and Welling, Max, As easy as APC: Leveraging self-supervised learning in the context Civic AI Lab focuses on the application of artificial intelligence in the fields of education, welfare, environment, mobility and health. We introduce a multi-agent equivariant policy network based on this factorization. Typically these models encode all features of the data into a single variable. I'm a PhD student at University of Amsterdam, under the supervision of Joris Mooij. Happy New Year and our thrilling AMLab Seminar will come back this Thursday! Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories. The Mercury Machine Learning Lab is a collaboration between University of Amsterdam, Delft University of Technology and Booking.com. The Amsterdam Machine Learning Lab (AMLab) conducts research in machine learning, artificial intelligence, and its applications to large scale data domains in science and industry. The public page is for the course Machine Learning 1. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. We demonstrate the flexibility of this framework by implementing advanced variational methods based on amortized Gibbs sampling and annealing. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at Microsoft Research (MSR). We have an external speaker Yuge Shi from Oxford University and you are all cordially invited to the AMLab Seminar on January 14th at 4:00 p.m. CET on Zoom, where Yuge will give a talk titled Multimodal Learning with Deep Generative Models. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. Amsterdam Machine Learning Lab / UvA-Bosch Delta Lab / SignLab Amsterdam. In addition, AIRLab Amsterdam will focus on talent development tracks. In this work, we leverage the newly introduced Topographic Variational Autoencoder to model the emergence of such localized category-selectivity in an unsupervised manner. Sim(2)-equivariance further improves performance on all tasks considered. All contributing authors are owner of their work and their publicly shared work islicensed under the Creative Commons license: AttributionNon commercialShare alike. Over the next five years, seven PhD researchers will work in the lab on projects that will focus, among other things, on achieving a quicker diagnosis of Alzheimers disease, modelling cardiac rhythms and on generating automatic reports based on X-ray images. Such vision strives to automatically interpret with the aid of deep learning what happens where, when and why in images and video. However, most NP variants place a strong emphasis on a global latent variable. Furthermore, through topographic organization over time (i.e. Please see our, We are delighted to announce that we have renewed our collaboration with Bosch through the. Find local Learning groups in Amsterdam and meet people who share your interests. 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. Sla de navigatie over en ga direct naar de inhoud, Research, Information and Statistics (OIS), Royal Netherlands Academy of Arts and Sciences. Powered by, the Bosch Center for Artificial Intelligence, AMLab will be presenting 8 papers at ICML 2022! Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories. Abstract: Machine learning, and more particularly, reinforcement learning, holds the promise of making robots more adaptable to new tasks and situations.However, the general sample inefficiency and lack of safety guarantees make reinforcement learning hard to apply directly to robotic systems.To mitigate the aforementioned issues, we focus on two aspects of the learning scheme.The first aspect regards robotic movements. Do Deep Generative Models Know What They Dont Know? This includes the development of deep generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. This includes the development of new methods for deep learning, probabilistic graphical models, Bayesian modeling, approximate inference, causal inference, reinforcement learning and the application of all of the above to large scale data domains in science and industry. My research has spanned a range of topics from generative modeling, variational inference, source compression, graph-structured learning to condensed matter physics. Welling is currently based at the University of Amsterdam and will be joining Microsoft Research in . We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels. Combining our estimator with REINFORCE, we obtain a policy gradient estimator and we reduce its variance using a built-in control variate which is obtained without additional model evaluations. Remarkably, this curation process can be used to understand three very different areas in deep learning: semi-supervised learning, out-of-distribution detection and the cold posterior effect. My interests are: causal inference, graphical models, structure learning . We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Specifically, on a synthetic dataset, we show that standard baselines are substantially improved upon through the use of APC, yielding the greatest gains in the combined setting of high missingness and severe class imbalance. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Before this he did a post-doc in applied differential geometry at the dept. See you there! The following is the information on this talk. You can buy my new book on AI here. Abstract: Much real-world data is sampled at irregular intervals, but most time series models require regularly-sampled data. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab . We evaluate our frameworks ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets. he directs the Amsterdam Machine Learning Lab (AMLAB), and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA). We propose a two-level hierarchical objective to control relative degree of statistical independence between blocks of variables and individual variables within blocks. We demonstrate the effectiveness of our method on several tasks in computational physics and chemistry and provide extensive ablation studies. Redactie: Chief Science Office, Gemeente Amsterdam. I was teaching assistant for the Master AI Reinforcement Learning 2019 and 2020 course at the University of Amsterdam. In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail. A number of recent efforts have focused on learning representations that disentangle statistically independent axes of variation by introducing modifications to the standard objective function. In order to obtain equivariance to arbitrary affine Lie groups we provide a continuous parameterisation of separable convolution kernels. Researchers at UvA will collaborate with Bosch researchers on topics including generative models, causal learning, geometric deep learning, uncertainty quantification in deep learning, human-in-the-loop methods, outlier detection, scene reconstruction, image decomposition, and semantic segmentation. Title:Partial local entropy and anisotropy in deep weight spaces. Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Machine learning Questions. Herke van Hoof, Patrick Forr, Eric Nalisnick, Erik Bekkers, Christian Naesseth, and Sara Magliacane serve as tenure-track faculty. See you there . In this paper, we present a method, which can partially alleviate this problem, by improving neural PDE solver sample complexity Lie point symmetry data augmentation (LPSDA). Moreover, AI algorithms have the potential to guide medical interventions accurately to the location of the tumor without damaging surrounding healthy tissue. The senior AI researchers leading an ICAI Lab in Amsterdam share the core values underlying their research in a living document. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. Our algorithm can be applied offline on human-demonstrated data, providing a safe scheme that avoids dangerous interaction with the real robot. The lab investigates how technology can deal with biases in data, account for multiple perspectives and subjective interpretations and bridge cultural differences. While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. See you there ! Deep learning is a form of machine learning with neural networks, loosely inspired by how neurons process information in the brain (see side bar). We are hiring seven #PhD students in computer #vision and machine #learning for the #QUVA Lab, a research collaboration between the University of #Amsterdam and #Qualcomm AI research. Terug Verzenden. Co-director of QUVA Lab& DELTA Lab Existing methods approach these problems separately, frequently making significant assumptions about the underlying data generation process in order to lessen the impact of missing information. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard t Hooft. The University of Amsterdam (UvA) is hiring an assistant professor in Computer Vision by Machine Learning for their QUVA Lab, a research collaboration with Qualcomm AI research. High levels of missing data and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data. Together the lab aims to develop state-of-the-art AI techniques to improve the safety in the Netherlands in a socially, legally and ethically responsible way. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. The Civic Artificial Intelligence Lab is a collaboration between City of Amsterdam, the University of Amsterdam, and the VU University Amsterdam. Our experiments demonstrate that SENs facilitate the application of equivariant networks to data with complex symmetry representations. To gain more insight into Bayesian deep learning and out-of-distribution detection, feel free to join and discuss it! From 2019 to 2021, I was a research assistant in the Approximate Bayesian Inference Team at the RIKEN Centre for Advanced Intelligence Project (Tokyo, Japan). To solve this, we perform probabilistic reasoning over the depth of neural networks. The usual parametrization of robotic movements allows high expressivity but is usually inefficient, as it covers movements not relevant to the task. One of the most well known examples of category-selectivity is the Fusiform Face Area (FFA), an area of the inferior temporal cortex in primates which responds preferentially to images of faces when compared with objects or other generic stimuli. We develop and use machine learning techniques to discover patterns in data streams produced by experiments in a wide variety of scientific fields, ranging . Furthermore, our loss function is also a consistent surrogate for multiclass L2D, like Mozannar & Sontags (2020). In this work, we leverage the newly introduced Topographic Variational Autoencoder to model of the emergence of such localized category-selectivity in an unsupervised manner. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus leverages the shared dynamics information. email: welling.max@gmail.com/m.welling@uva.nl Bekkers is a person. Our experiments demonstrate that conjugate EBMs achieve competitive results in terms of image modelling, predictive power of latent space, and out-of-domain detection on a variety of datasets. Title: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. Senior Fellow Canadian Institute for Advanced Research A collaboration between IIAI and the University of Amsterdam. Most proposed flow models therefore either restrict to a function class with easy evaluation of the Jacobian determinant, or an efficient estimator thereof. These approaches generally assume a simple diagonal Gaussian prior and as a result are not able to reliably disentangle discrete factors of variation. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates on both continuous (atom coordinates) and categorical features (atom types). AI solution can assist medical specialists finding and applying the right treatment based on all this information. Deep Reinforcement Learning Reading Group, https://github.com/google-research/torchsde. For comments and amendments please contactopenresearch@amsterdam.nl, Voor op- en aanmerkingen neem contact op met. QUVA Lab houses several projects, from Federated Learning, Deep Compression, Combinatorial Optimization, Causal Representations Learning, to Video . Stichting Kinderen Kankervrij, actienummer 9953), Research Chair & Full Professor AMLAB, UvA. And this should happen at each and every treatment session (which varies from 3 to 35). Thus, we are presented with a proverbial chicken-and-egg problem. PhD defence Lynn Srensen (Machine Learning) Start: 2023-01-17 15:00:00+01:00 End: 2023-01-17 16:00:00+01:00. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to enabling medical diagnoses. Everybody is welcome to attend the public defence of Lynn Srensen of her thesis entitled 'Deep Neural Network Models of Visual Cognition'. Looking for Laboratory jobs in Amsterdam? We have a guest speaker Michal Defferrard from cole Polytechnique Fdrale de Lausanne (EPFL) and you are all cordially invited to the AMLab Seminar on February 25th (Thursday) at 4:00 p.m. CET on Zoom. Hi, everyone! Amsterdam Machine Learning Lab University of Amsterdam m.welling@uva.nl Abstract We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds and graphs, which is equivariant under continuous 3D roto-translations. In scientific applications, domain knowledge can give a linear approximation of the latent transition maps, which we can easily incorporate into our model. A collaboration between the Dutch Police, Utrecht University, University of Amsterdam and Delft University of Technology. Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory). David also co-founded Invenia, an energy forecasting and trading company. Lid worden en connectie maken University of Amsterdam. Title : Multimodal Learning with Deep Generative Models. Project manager for ICAI Labs Amsterdam: Jeanne Kroeger (j.c.l.kroeger@uva.nl). of Applied Mathematics at Technical University Eindhoven (TU/e). Usage of such domain knowledge is reflected in excellent results (despite our models simplicity) on the chaotic Lorenz system compared to fully supervised and variational inference methods. Post your CV Free. Machine learning is marking a revolution in the world. This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Empirical results demonstrate MoE-NPs strong generalization capability to unseen tasks in these benchmarks. The Amsterdam Machine Learning Lab (AMLab) conducts research in machine learning, artificial intelligence, and its applications to large scale data domains in science and industry. Office Phone: 0205258256 Distinguished Scientist at Microsoft Research 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. Do Deep Gen. Models Know What They Don't Know? The AI4Science Lab is also connected to AMLAB, the Amsterdam Machine Learning Lab. Academics in turn gain a better understanding of how AI is used to innovate research platforms to solve real-world societal problems. To gain more insight into Graph Deep Learning, feel free to join and discuss it! And then Daniele will present a recent work titled Partial local entropy and anisotropy in deep weight spaces. Science Park 904 The Mercury Machine Learning Lab is a collaboration between University of Amsterdam, Delft University of Technology and Booking.com. The researchers in the lab work on techniques across the full breadth of AI. The research projects cover fundamental research topics, ranging from model-based exploration, parallel model-based reinforcement learning, methods for combined online and offline evaluation, prediction methods that correct for undesired feedback loops and selection bias, domain generalization and domain adaptation, and novel language processing models for better generalization. Editorial board: Chief Science Office, Gemeente Amsterdam. A collaboration between City of Amsterdam, the University of Amsterdam, and the VU University Amsterdam. ditorial board: Chief Science Office, Gemeente Amsterdam. Paper Link: https://arxiv.org/pdf/2003.04630.pdf. Amsterdam Machine Learning Lab conducts research in the area of large scale modelling of complex data sources. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. In addition, we also proposed 4 criteria (with evaluation metrics) that multi-modal deep generative models should satisfy; in the second work, we designed a contrastive-ELBO objective for multi-modal VAEs that greatly reduced the amount of paired data needed to train such models. This enables us to train a single, amortized model that infers causal relations across samples with different underlying causal graphs, and thus makes use of the information that is shared. This includes the development of new methods for probabilistic graphical models and non-parametric Bayesian models, the development of faster (approximate) inference and learning methods, deep learning, causal inference, reinforcement learning and multi-agent systems and .