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. 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). Max Welling and Jan-Willem van de Meent serve as co-directors. The public page is for the course Machine Learning 1. The new loss functions are referred to as partial local entropies. Group convolutional neural networks (G-CNNs) have been shown to increase parameter efficiency and model accuracy by incorporating geometric inductive biases. Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry. 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. Selected Publications. A collaboration between IIAI and the University of Amsterdam. Powered by, the Bosch Center for Artificial Intelligence, AMLab will be presenting 8 papers at ICML 2022! These approaches generally assume a simple diagonal Gaussian prior and as a result are not able to reliably disentangle discrete factors of variation. We use these theoretical insights to derive a simple algorithm that is able to select data augmentation techniques that will lead to better domain generalization. Microsoft is opening a new research lab in Amsterdam headed by Max Welling, one of the world's leading researchers in machine learning. Max Welling is 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 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. 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 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. 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. Title : Depth Uncertainty in Neural Networks. A collaboration between City of Amsterdam, the University of Amsterdam, and the VU University Amsterdam. Calibrated Learning to Defer with One-vs . More email: welling.max@gmail.com/m.welling@uva.nl In this work, we leverage the newly introduced Topographic Variational Autoencoder to model the emergence of such localized category-selectivity in an unsupervised manner. 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 . Model-based meta reinforcement learning addresses these issues by learning dynamics and leveraging knowledge from prior experience. Happy New Year and our thrilling AMLab Seminar will come back this Thursday! By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. 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. It is a major challenge to give patients the right dose of radiation, at the right spot with least damage to healthy tissue, and while the patient and the tumor move and change shape during radiation and over time. Research projects in the lab will focus on learning to recognize objects in images from a single example, personalized event detection and summarization in video, and privacy preserving deep learning. 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. 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. Attila Szabo is a machine learning engineer at NICO.LAB. We have a guest speaker for our Seminar, and you are all cordially invited to the AMLab Seminar onThursday 3rdDecember at 16:00 CETon Zoom, whereMiles Cranmerwill give a talk titledLAGRANGIAN NEURAL NETWORKS. Discovery Lab is a collaboration between Elsevier, the University of Amsterdam and VU University Amsterdam. You can buy my new book on AI here. To gain more insight into Causal Discovery, feel free to join and discuss it! Mart Van Blokland has a Bachelor of Science from Amsterdam University College, a . 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. This finding motivates further weight-tying by sharing convolution kernels over subgroups. See you there! Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. We show that our estimator can be derived as the Rao-Blackwellization of three different estimators. Our colleague Sindy Lwe will present her recent work at our AMLab Seminar and you are all cordially invited to this thrilling session on March 4th (Thursday) at 4:00 p.m. CET on Zoom. Post your CV Free. Erik Bekkers is an assistant professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam (AMLab, UvA). To encode such symmetries while still allowing distributed execution we propose a factorization that decomposes global symmetries into local transformations. A lot of complex information is acquired from patients during and prior to the treatment through medical imaging, pathology, DNA, and so on. We have a guest speaker Daniele Musso from Universidad de Santiago de Compostela and Daniele will give a talk at our Lab. But symmetries alone might not be enough: for example, social networks, finite grids, and sampled spheres have few automorphisms. 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. Title: Learning from graphs: a spectral perspective. We encourage you to take a look and provide feedback. . Experimentally, we demonstrate our model yields spatially dense neural clusters selective to faces, bodies, and places through visualized maps of Cohens d metric. We will be . In the context of PDEs, it turns out that we are able to quantitatively derive an exhaustive list of data transformations, based on the Lie point symmetry group of the PDEs in question, something not possible in other application areas. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. We perform approximate inference in state-space models with nonlinear state transitions. However, these restrictions limit the performance of such density models, frequently requiring significant depth to reach desired performance levels. Abstract: Image classification datasets such as CIFAR-10 and ImageNet are carefully curated to exclude ambiguous or difficult to classify images. Abstract: In this talk, I will present my two works on multi-modal representation learning using deep generative models. My research has spanned a range of topics from generative modeling, variational inference, source compression, graph-structured learning to condensed matter physics. Other faculty inAMLabinclude Ben Krse (professor at the Hogeschool Amsterdam) doingresearch in ambient robotics, Dariu Gavrila (Daimler) known for hisresearch in human aware intelligence and Zeynep Akata (scientific co-director of Delta Lab and co-affiliated with Max Planck Institute for Informatics) doing research on machine learning applied to the intersection of vision and language. You are expected to work on fundamental aspects of computer vision by machine learning, deep learning models, and algorithms. 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. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. I am a 5th year PhD student in the AMLab, advised by Professor Jan-Willem van de Meent. David also co-founded Invenia, an energy forecasting and trading company. 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 . 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). 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). In this work, we examine the assumptions behind this method, particularly in conjunction with model selection. Deep Reinforcement Learning Reading Group, https://github.com/google-research/torchsde. In this work, we instead demonstrate how a general self-supervised training method, namely Autoregressive Predictive Coding (APC), can be leveraged to overcome both missing data and class imbalance simultaneously without strong assumptions.
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