amsterdam machine learning lab

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. 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 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. 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). Title:Partial local entropy and anisotropy in deep weight spaces. On-policy gradient estimators are usually easy to obtain, but they are, due to their nature, sample inefficient. 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. These include preserving structural information with adversarial learning for near real-time applications, minimizing performance disparity . 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. Title : Selecting Data Augmentation for Simulating Interventions. We analyze how to focus the representation of only those movements relevant to the considered task. We introduce a multi-agent equivariant policy network based on this factorization. I was teaching assistant for the Master AI Reinforcement Learning 2019 and 2020 course at the University of Amsterdam. Atlas Lab will focus on using Artificial Intelligence (AI) for developing advanced, highly accurate and safe high definition (HD) maps for self-driving vehicles. I am a 5th year PhD student in the AMLab, advised by Professor Jan-Willem van de Meent. We introduce Steerable E(3) Equivariant Graph Neural Networks (SEGNNs) that generalise equivariant graph networks, such that node and edge attributes are not restricted to invariant scalars, but can contain covariant information, such as vectors or tensors. This website is work in progress. We provide theoretical support for our recommendations and validate them empirically on MLPs, classic CNNs, residual networks with and without normalisation layers, generative autoencoders and transformers. To bridge the gap between theory and practice, we develop a causal perspective on the problem of domain generalization. I started my PhD at Northeastern University where I was for 4 years before transferring to University of Amsterdam. temporal coherence), we demonstrate how predefined latent space transformation operators can be encouraged for observed transformed input sequences a primitive form of unsupervised learned equivariance. Title: A statistical theory of cold-posteriors, semi-supervised learning and out-of-distribution detection. In this paper, we propose conjugate energy-based models (CEBMs), a new class of energy-based models that define a joint density over data and latent variables. 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. A collaboration between City of Amsterdam, the University of Amsterdam, and the VU University Amsterdam. In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. 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. To gain more deep insights into Multi-modal Learning, feel free to join and discuss it! Core research themes revolve around public values such as diversity and inclusivity. 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. 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. We perform approximate inference in state-space models with nonlinear state transitions. Postbox 94323 Since I'm currently looking for Ph.D. positions in Europe, specifically outside of Germany + Switzerland, I wanted to know 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. 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. Research Chair & Full Professor AMLAB, UvA. Furthermore, through topographic organization over time (i.e. Microsoft to open research lab in Amsterdam. I will discuss how spectral graph theory yields vertex representations and a generalized convolution that shares weights beyond symmetries. Group convolution layers are easy to use and can be implemented with negligible computational overhead for discrete groups generated by translations, reflections and rotations. 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. 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. I got my MSc in Data Science at the University of Edinburgh. Yet even though neural network models see increasing use in the physical sciences, they struggle to learn these symmetries. . This collaboration allows Elseviers data scientists to work closer with data scientists in academia, contribute to education and science, and pursue a PhD. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. See you there ! A collaboration between the Netherlands Cancer Institute and UvA. A collaboration between Booking.com, TU Delft and University of Amsterdam. And then Samuele will give a talk titled Movement Representation and Off-Policy Reinforcement Learning for Robotic Manipulation. We validate our approach on real-world regression and image classification tasks. Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings. Faculty of Science In this paper, we take a closer look at this framework and propose a new posterior sampling based approach that consists of a new model to identify task dynamics together with an amortized policy optimization step. 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. Amsterdam Machine Learning lab. 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. And then Sindy will give a talk titled Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. A list of 29 interesting Machine learning questions! 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 new loss functions are referred to as partial local entropies. Specifically, different samples may share the dynamics which describe the effects of their causal relations. We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives. 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. We show that our objective is effective on multiple SOTA multi-modal VAEs and on different datasets, and showed that only 20% of data is needed to achieve similar performance to a model trained on the original objective. Neural networks are increasingly being used to solve partial differential equations (PDEs), replacing slower numerical solvers. To gain more insight into Graph Deep Learning, feel free to join and discuss it! 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. The AI for Retail (AIR) Lab Amsterdam is a joint UvA-Ahold Delhaize industry lab and will conduct research into socially responsible algorithms that can be used to make recommendations to consumers and into transparent AI technology for managing goods flows. Research projects in the lab . He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. 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. Deadline : 16 October 2022. The usual parametrization of robotic movements allows high expressivity but is usually inefficient, as it covers movements not relevant to the task. Do Deep Gen. Models Know What They Don't Know? Empirical results demonstrate MoE-NPs strong generalization capability to unseen tasks in these benchmarks. 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). Experimentally, we demonstrate our model yields spatially dense neural clusters selective to faces, bodies, and places through visualized maps of Cohens d metric. Continuous-time models address this problem, but until now only deterministic (ODE) models or linear-Gaussian models were efficiently trainable with millions of parameters. The Civic Artificial Intelligence Lab is a collaboration between City of Amsterdam, the University of Amsterdam, and the VU University Amsterdam. In this work, we investigate the properties of representations learned by regular G-CNNs, and show considerable parameter redundancy in group convolution kernels. Office Phone: 0205258256 We present this framework and demonstrate how it can easily be deployed to improve neural PDE solver sample complexity by an order of magnitude. Paper Link: https://arxiv.org/pdf/2003.04630.pdf. Looking for Laboratory jobs in Amsterdam? "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Erik Bekkers is an assistant professor in Geometric Deep Learning in the Machine Learning Lab of the University of Amsterdam (AMLab, UvA). The result is a framework for user-programmable variational methods that are correct by construction and can be tailored to specific models. The mission of UvA-Bosch Delta Lab (Deep Learning Technologies Amsterdam) is to perform world class research in machine learning and computer vision. Terug Verzenden. 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. 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. A collaboration between Bosch and the University of Amsterdam. We demonstrate the flexibility of this framework by implementing advanced variational methods based on amortized Gibbs sampling and annealing. This includes the development of deep generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference . The AI4Science Lab is also connected to AMLAB, the Amsterdam Machine Learning Lab. Both institutes join forces in the development of AI algorithms to improve cancer treatment. The lab also serves as an information point for residents and businesses who have questions about new technologies and the ethical and inclusive use of them. (donations toNL89INGB0000008118 t.n.v. 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 Amsterdam Machine Learning Lab (AMLab) conducts research in the area of large scale modelling of complex data sources. We discuss ours and related work through the lens of equivariant non-linear convolutions, which further allows us to pin-point the successful components of SEGNNs: non-linear message aggregation improves upon classic linear (steerable) point convolutions; steerable messages improve upon recent equivariant graph networks that send invariant messages. Our proposed factorization allows for distributing the computation that enforces global symmetries over local agents and local interactions. On time-series data, most causal discovery methods fit a new model whenever they encounter samples from a new underlying causal graph. 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 . 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 the area of large scale modelling of complex data sources. To gain more deep insights into Uncertainty in Deep Neural Networks, feel free to join and discuss it! The senior AI researchers leading an ICAI Lab in Amsterdam share the core values underlying their research in a living document. I am a PhD student with Eric Nalisnick in the Amsterdam Machine Learning Lab . Title: Learning from graphs: a spectral perspective. Everybody is welcome to attend the public defence of Lynn Srensen of her thesis entitled 'Deep Neural Network Models of Visual Cognition'. Experiments on a variety of datasets demonstrate that our objective can not only disentangle discrete variables, but that doing so also improves disentanglement of other variables and, importantly, generalization even to unseen combinations of factors. Abstract: Image classification datasets such as CIFAR-10 and ImageNet are carefully curated to exclude ambiguous or difficult to classify images. The following is the information on this talk. Before this he did a post-doc in applied differential geometry at the dept. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. 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. We demonstrate that this model successfully learns sets of approximately equivariant features (i.e. We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples. ditorial board: Chief Science Office, Gemeente Amsterdam. AI plays a crucial role in analysing digitised cultural collections and making them accessible. 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). We propose Amortized Causal Discovery, a novel framework that leverages such shared dynamics to learn to infer causal relations from time-series data. These topics are both of fundamental scientific importance, as well as of immediate practical relevance for modern online businesses like Booking that aim to maximize customer satisfaction in quickly changing markets with the help of sophisticated data analytics. Join a group and attend online or in person events. 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.

Coffee Shop Grapevine Main Street, General Skin Minecraft, Access-control-allow-origin Missing Header, Music Education Budget Cuts, Claptone The Masquerade @ Pacha Ibiza Opening Tracklist, Tree Treatment Services Near Me,