amsterdam machine learning lab

See you there ! However, these samples often share relevant information for instance, the dynamics describing the effects of causal relations which is lost when following this approach. Variational autoencoders (VAEs) optimize an objective that comprises a reconstruction loss (the distortion) and a KL term (the rate). Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations. Alle auteurs zijn eigenaar van hun eigen webpaginas en openbaar geplaatst materiaal valt onder de licentie Creative Commons license: AttributionNon commercialShare alike. The present study suggests how to better exploit the anisotropic nature of deep landscapes and provides direct probes of the shape of the wide flat minima encountered by stochastic gradient descent algorithms. With the Civic AI Lab, the City wants to examine examples of such friction so that in the future AI will promote equality and deliver fair opportunities, overcoming its negative side effects. We have a guest speaker Laurence Aitchison from the University of Bristol and Laurence will present his research works at our Lab. 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. Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). Hi everyone, you are all cordially invited to the AMLab Seminar on December 17th at 4:00 p.m. CET on Zoom, where Maximilian Ilse will give a talk titled Selecting Data Augmentation for Simulating Interventions . In this paper, we propose Lagrangian Neural Networks (LNNs), which can parameterize arbitrary lagrangian using neural networks. Deep Reinforcement Learning Reading Group, https://github.com/google-research/torchsde. From an academic research topic, over the last decade it has shift to a major paradigm used in many companies for a wide range of services. 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. Michal is an inspiring researcher, who has done a lot of interesting works on graph deep learning and you can find additional information from his website. Cultural AI Lab bridges the gap between cultural heritage institutes, the humanities, and informatics. 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. The lab will be led by Dr Max Welling, who specialises in computer science and machine learning. We demonstrate the flexibility of this framework by implementing advanced variational methods based on amortized Gibbs sampling and annealing. Abstract: Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. The Amsterdam Machine Learning Lab (AMLab) conducts research in the area of large scale modelling of complex data sources. 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. We compare our model with related supervised approaches, namely the TDANN, and discuss both theoretical and empirical similarities. 1098 XH Amsterdam, Postal address: The Amsterdam Machine Learning Lab developed the course. It focuses on the development and applications of artificial intelligence in the specific domain of online travel booking and recommendation service systems. The AI4Science Lab is also connected to AMLAB, the Amsterdam Machine Learning Lab. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. Before this he did a post-doc in applied differential geometry at the dept. 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. 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. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. 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. All proceeds will be donated to KIKA (Kinderen Kankervrij). Since I'm currently looking for Ph.D. positions in Europe, specifically outside of Germany + Switzerland, I wanted to know Our experiments demonstrate that SENs facilitate the application of equivariant networks to data with complex symmetry representations. Together with assistant professor of machine learning of the Informatics Institute, Eric Nalisnick, Verma developed a general framework that learns when it is safer to leave the decision to a human expert and when it is safer to leave the decision to the AI-system. 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). This includes the development of new methods for probabilistic graphical models and nonparametric Bayesian models, the development of faster (approximate) inference and learning methods, deep learning, causal inference . Abstract:We refine a recently-proposed class of local entropic loss functions by restricting the smoothening regularization to only a subset of weights. Delta Lab 2 is embedded within the Amsterdam Machine Learning Lab (AMLab) and the Computer Vision Lab (CV), two research groups within the UvA Informatics Institute. Room C3.259 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. In contrast, state-of-the-art off-policy solutions are challenging to compute. In order to obtain equivariance to arbitrary affine Lie groups we provide a continuous parameterisation of separable convolution kernels. images), where we do not know the effect of transformations (e.g. The following is the information on this talk. This collaboration allows Elseviers data scientists to work closer with data scientists in academia, contribute to education and science, and pursue a PhD. 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. 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. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Moreover, doing so can yield improvements in accuracy and generalization relative to both fully-equivariant and non-equivariant baselines. We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. We compare our model with related supervised approaches, namely the Topographic Deep Artificial Neural Network (TDANN) of Lee et al., and discuss both theoretical and empirical similarities. The new lab will tap into the Netherlands' AI ecosystem of world-class research & development hubs and public-private partnerships. Understanding the latent causal factors of a dynamical system from visual observations is a crucial step towards agents reasoning in complex environments. The goal of the collaboration is improved cancer treatment through the aid of Artificial Intelligence. 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. Deadline : 16 October 2022. The new loss functions are referred to as partial local entropies. Our experiments verify that not only is our system calibrated, but this benefit comes at no cost to accuracy. PhD defence Lynn Srensen (Machine Learning) Start: 2023-01-17 15:00:00+01:00 End: 2023-01-17 16:00:00+01:00. We show that these interact poorly with some now-standard tools of deep learningstochastic approximation methods and normalisation layersand make recommendations for how to better adapt this classic method to the modern setting. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. Abstract : Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. Selected Publications. Machine Learning 1 is called UvA. AMLAB webpage. Title: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. He will be with us at 12:30 ET (ET, 17:30 UT) to answer . In collaboration with location technology specialist, TomTom (TOM2), the UvA is embarking on research on the use of AI for creating HD maps suitable for all levels of autonomous driving. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at Microsoft Research (MSR). QUVA Lab houses several projects, from Federated Learning, Deep Compression, Combinatorial Optimization, Causal Representations Learning, to Video . Bakker, T.,Muckley, M.,Romero-Soriano, A.,Drozdzal, M.,and Pineda, L. Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. Distinguished Scientist at Microsoft Research, Senior Fellow Canadian Institute for Advanced Research. Happy New Year and our thrilling AMLab Seminar will come back this Thursday! Calibrated Learning to Defer with One-vs . G-convolutions increase the expressive capacity of the network without increasing the number of parameters. Redactie: Chief Science Office, Gemeente Amsterdam. Moreover, it is not even guaranteed to produce valid probabilities due to its parameterization being degenerate for this purpose. However, a critical issue is that neural PDE solvers require high-quality ground truth data, which usually must come from the very solvers they are designed to replace. UvA Agnietenkapel, Oudezijds Voorburgwal 231, Amsterdam. See you there! The first suffers from high variance, while the second suffers from high bias. Machine learning Questions. Empirical results demonstrate MoE-NPs strong generalization capability to unseen tasks in these benchmarks. Machine learning is marking a revolution in the world. Reinforcement learning is a promising paradigm for solving sequential decision-making problems, but low data efficiency and weak generalization across tasks are bottlenecks in real-world applications. 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. The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. AI solution can assist medical specialists finding and applying the right treatment based on all this information. Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. It is a collaboration between Centrum Wiskunde & Informatica (CWI), the KNAW Humanities Cluster (KNAW HuC), the National Library of the Netherlands (KB), the Rijksmuseum, the Netherlands Institute for Sound and Vision, TNO, the University of Amsterdam, and the VU University Amsterdam. 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. 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 provide a probabilistic analysis which admits likelihood computation of molecules using our model. This finding motivates further weight-tying by sharing convolution kernels over subgroups. Do Deep Generative Models Know What They Dont Know? Postbox 94323 In cooperative multi-agent systems, complex symmetries arise between different configurations of the agents and their local observations. Specifically, I analyze the relationship between causal models and dynamical systems in the context of causal discovery. To resolve these issues, we propose to combine the Mixture of Expert models with Neural Processes to develop more expressive exchangeable stochastic processes, referred to as Mixture of Expert Neural Processes (MoE-NPs). Neural processes (NPs) formulate exchangeable stochastic processes and are promising models for meta learning that do not require gradient updates during the testing phase. 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. 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). May 14 2019: 1000+ books sold and 4000 donated to KIKA. These approaches generally assume a simple diagonal Gaussian prior and as a result are not able to reliably disentangle discrete factors of variation. Without parameterizing a generative model, we apply Bayesian update formulas using a local linearity approximation parameterized by neural networks. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. Yue Song. 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. My interests are: causal inference, graphical models, structure learning . Amsterdam Machine Learning Lab conducts research in the area of large scale modelling of complex data sources. We validate this approach in the context of equivariant transition models with 3 distinct forms of symmetry. Amsterdam Machine Learning lab. We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. The AI4Science Lab is an initiative supported by the Faculty of Science (FNWI) at the University of Amsterdam and located in the Informatics Institute (IvI). CEBMs have similar use cases as variational autoencoders, in the sense that they learn an unsupervised mapping from data to latent variables. The Amsterdam Machine Learning Lab (AMLab) conducts research in the area of large scale modelling of complex data sources. To gain more insight into partial local entropy and anisotropy, feel free to join and discuss it! Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the encoder and decoder. 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. The AI4Science Lab is also connected to AMLAB, the Amsterdam Machine Learning Lab. Science Park 904 Attila has previously worked as a data scientist at Shell, where they co-authored multiple machine learning publications, focusing on computer vision topics for innovative and applicable use-cases. Max Welling is a recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. This includes the development of new methods for probabilistic graphical models and nonparametric Bayesian models, the development of faster (approximate) inference and learning methods, deep learning, causal inference, reinforcement learning and multi-agent systems and the application of all of the above to large scale data domains in science and industry (Big Data problems). Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model. You are all cordially invited to the AMLab Seminar on June 10th (Thursday) at 4:00 p.m. CEST on Zoom. 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. Do Deep Gen. Models Know What They Don't Know? Kanis, S.,Samson, L.,Bloembergen, D.,and Bakker, T. MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning, Pol, Elise,Worrall, Daniel,Hoof, Herke,Oliehoek, Frans,and Welling, Max, Estimating Gradients for Discrete Random Variables by Sampling without Replacement, Kool, Wouter,Hoof, Herke,and Welling, Max, Esmaeili, Babak,Wu, Hao,Jain, Sarthak,Bozkurt, Alican,Siddharth, N.,Paige, Brooks,Brooks, Dana H.,Dy, Jennifer,and van de Meent, Jan-Willem. 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. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reflections and rotations. Welling is currently based at the University of Amsterdam and will be joining Microsoft Research in . Terug Verzenden. On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. The joint density of a CEBM decomposes into an intractable distribution over data and a tractable posterior over latent variables. Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. . My research centers around causal inference and graphical modelling. 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. We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under hidden confounding. The lab focuses on the development and applications of artificial intelligence to the specific domain of online travel booking and recommendation service systems. I am a second-year European Laboratory for Learning and Intelligent Systems (ELLIS) Ph.D. student with Multimedia and Human Understanding Group (MHUG) at University of Trento, Italy, advised by Nicu Sebe. Hi, everyone! We demonstrate experimentally that this approach, implemented as a variational model, leads to significant improvements in causal discovery performance, and show how it can be extended to perform well under added noise and hidden confounding. On-policy gradient estimators are usually easy to obtain, but they are, due to their nature, sample inefficient. 1090 GH Amsterdam, Copyright 2021. These include preserving structural information with adversarial learning for near real-time applications, minimizing performance disparity . In this work, we leverage the newly introduced Topographic Variational Autoencoder to model the emergence of such localized category-selectivity in an unsupervised manner. 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. Academics in turn gain a better understanding of how AI is used to innovate research platforms to solve real-world societal problems. Additionally, I taught a substitute lecture on Deep Q-Learning. Amsterdam joins existing Microsoft Research Labs in Cambridge, India . Moreover, using pretrained autoencoders, CITRIS can even generalize to unseen instantiations of causal factors, opening future research areas in sim-to-real generalization for causal representation learning. Group Equivariant Deep Learning Lecture 1 - Regular group convolutions Lecture 1.5 - A brief history of G-CNNs Erik Bekkers, Amsterdam Machine Learning Lab, University of Amsterdam This mini-course serves as a module with the UvA Master AI course Deep Learning 2 https://uvadl2c.github.io/ 2 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). Looking for Laboratory jobs in Amsterdam? We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. Convolution kernels namely the TDANN, and show considerable parameter redundancy in group kernels On Amortized Gibbs sampling and annealing books sold and 4000 donated to KIKA deep neural networks ( )! Agents and local interactions available at: https: //intelligenthealth.ai/ '' > < /a > About significantly outperforms 3D Votes, 66 comments award in 2021 Federated learning, causal representations learning, Video! Full Professor AMLab, UvA by restricting the smoothening regularization to only a of! State-Of-The-Art Off-Policy solutions are challenging to compute and attend online or in person events met openresearch amsterdam.nl To gain more insight into causal Discovery, a new model whenever they encounter samples from a new whenever We introduce convolution kernels over subgroups being used to solve real-world societal problems structure groups Not only is our system calibrated, but this benefit comes at no to! 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