We analyze how to focus the representation of only those movements relevant to the considered task. of time series classification with varying levels of sparsity and For comments and amendments please contactopenresearch@amsterdam.nl, Voor op- en aanmerkingen neem contact op met. Additionally, using an approximate conditional independence, we can perform smoothing without having to parameterize a separate model. 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. 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. 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. The result is a framework for user-programmable variational methods that are correct by construction and can be tailored to specific models. Furthermore, by introducing a normalizing flow, CITRIS can be easily extended to leverage and disentangle representations obtained by already pretrained autoencoders. Hi everyone! In addition, we provide a probabilistic analysis which admits likelihood computation of molecules using our model. The new loss functions are referred to as partial local entropies. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. severe class imbalance, Wever, Fiorella,Keller, T. Anderson,Garcia, Victor,and Symul, Laura, Variational combinatorial sequential Monte Carlo methods for Bayesian phylogenetic inference, Moretti, Antonio Khalil,Zhang, Liyi,Naesseth, Christian A.,Venner, Hadiah,Blei, David,and Peer, Itsik, Rate-Regularization and Generalization in Variational Autoencoders, Bozkurt, Alican,Esmaeili, Babak,Tristan, Jean-Baptiste,Brooks, Dana,Dy, Jennifer,and Meent, Jan-Willem, Zimmermann, Heiko,Wu, Hao,Esmaeili, Babak,and Meent, Jan-Willem, Wu, Hao*,Esmaeili, Babak*,Wick, Michael,Tristan, Jean-Baptiste,and van de Meent, Jan-Willem, Learning proposals for probabilistic programs with inference combinators, Stites, Sam,Zimmermann, Heiko,Wu, Hao,Sennesh, Eli,and Meent, Jan-Willem, Nalisnick, Eric,Gordon, Jonathan,and Miguel Hernandez-Lobato, Jose, Bayesian Deep Learning via Subnetwork Inference, Daxberger, Erik,Nalisnick, Eric,Allingham, James U,Antoran, Javier,and Hernandez-Lobato, Jose Miguel, Normalizing Flows for Probabilistic Modeling and Inference, Papamakarios, George,Nalisnick, Eric,Rezende, Danilo Jimenez,Mohamed, Shakir,and Lakshminarayanan, Balaji, Optimizing Adaptive Notifications in Mobile Health Interventions Systems: Reinforcement Learning from a Data-driven Behavioral Simulator, Wang, Shihan,Zhang, Chao,Krse, Ben,and Hoof, Herke, Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study. We develop a generative model of dataset curation in which multiple annotators label every image, with the image being included in the dataset only if all the annotators agree. I did my BSc in Artificial Intelligence and . Max Welling and Jan-Willem van de Meent serve as co-directors. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. 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. The lab also engages in cross-disciplinary collaborations through the AI4Science Lab. A collaboration between the Dutch Police, Utrecht University, University of Amsterdam and Delft University of Technology. The lab participates in partnerships with industry through the QUvA Lab (with Qualcomm) and the Delta Lab (with Bosch). Title: Movement Representation and Off-Policy Reinforcement Learning for Robotic Manipulation. We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. The Language Technology Lab at the Informatics Institute of the. 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. R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. 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. US-based Microsoft Research is set to open an artificial intelligence lab in Amsterdam, which will focus on molecular simulation under the leadership of renowned Dutch physicist Max Welling. 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). Title : Multimodal Learning with Deep Generative Models. The Amsterdam Machine Learning Lab (AMLab) conducts research in the area of large scale modelling of complex data sources. 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. Then we apply MoE-NPs to both few-shot supervised learning and meta reinforcement learning tasks. Abstract: Much real-world data is sampled at irregular intervals, but most time series models require regularly-sampled data. Abstract: Image classification datasets such as CIFAR-10 and ImageNet are carefully curated to exclude ambiguous or difficult to classify images. We are very happy to have Manfred van der Voort from icr3ate.nl, the ICR3ATE | Digital Makers Lab in Ede, sharing his recent earliest experiences on (the application of) the IBM Watson platform.In his talk he will elaborate on utilizing Artificial Intelligence & Machine Learning in application domains like image recognition, language understanding and data analytics. Abstract: Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph. I am a PhD student with Eric Nalisnick in the Amsterdam Machine Learning Lab . Paper Link: https://arxiv.org/pdf/2005.01856.pdf. 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. See you there! Atlas Lab will focus on using Artificial Intelligence (AI) for developing advanced, highly accurate and safe high definition (HD) maps for self-driving vehicles. This includes the development of deep generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference . 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. I got my MSc in Data Science at the University of Edinburgh. 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. A collaboration between Elsevier, the University of Amsterdam, and the VU University Amsterdam. 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. You are all cordially invited to the AMLab Seminar on December 10th at 4:00 p.m. CET on Zoom, where Javier Antorn and James Allingham will give a talk titled Depth Uncertainty in Neural Networks . In addition, if we marginalise over the class-label, we get a semi-supervised learning objective mirroring entropy minimization and pseudo-labelling, which allows us to use unlabelled points to improve the performance of a classifier (very early version: arxiv.org/abs/2008.05913). Our algorithm can be applied offline on human-demonstrated data, providing a safe scheme that avoids dangerous interaction with the real robot. Attila Szabo is a machine learning engineer at NICO.LAB. Cultural AI Lab bridges the gap between cultural heritage institutes, the humanities, and informatics. I am a 5th year PhD student in the AMLab, advised by Professor Jan-Willem van de Meent. Please see our, We are delighted to announce that we have renewed our collaboration with Bosch through the. I started my PhD at Northeastern University where I was for 4 years before transferring to University of Amsterdam. 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. The learning to defer (L2D) framework has the potential to make AI systems safer. He is a fellow and founding board member of the European Lab for learning and Intelligent systems (ELLIS). Do Deep Generative Models Know What They Dont Know? This novel representation has the effect of ameliorating the sample efficiency and providing higher safety.The low quality of a gradient estimator in reinforcement learning can cause another source of inefficiency. The University of Amsterdam has an Artificial Intelligence master program. Title:Partial local entropy and anisotropy in deep weight spaces. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. 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. The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important. Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner. Atlas Lab is a collaboration between TomTom and the University of Amsterdam. Post your CV Free. You are all cordially invited to the AMLab Seminar on June 10th (Thursday) at 4:00 p.m. CEST on Zoom. 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. You are all cordially invited to the AMLab Seminar on April 8th (Thursday) at 4:00 p.m. CEST on Zoom. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. 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. Machine learning Questions. Paper Link: https://arxiv.org/pdf/2003.04630.pdf. To gain more deep insights into neural stochastic differential equations, feel free to join and discuss it! Experimentally, the proposed method significantly outperforms previous 3D molecular generative methods regarding the quality of generated samples and efficiency at training time. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels.
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