Sindy Löwe wins UvA Thesis Prize 2020

In her winning thesis, Sindy Löwe describes an original and innovative concept within Artificial Intelligence, which she conceived and developed independently. It concerns a new algorithm for training neuronal networks and gives a better explanation for and good insight in the way cerebral neuronal networks learn. The jury believes that Löwe shows the ability to develop a groundbreaking and innovative idea at a high academic level in a scientifically sound manner.

Abstract: Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference with weaker assumptions than the classical methods require.

Chris Olah is best known for DeepDream, the Distill journal, and his blog. He spent five years at Google Brain, where he focused on neural network interpretability and safety. He’s also worked on various other projects, including early TensorFlow, generative models, and NLP. Prior to Google Brain, Chris dropped out of university and did deep learning research independently as a Thiel Fellow. Chris recently joined OpenAI to start a new interpretability team there.

PhD position in Machine Learning and Deep Learning. This position is funded by Bosch Research, is within UvA-Bosch Delta Lab whose research focuses on deep learning and applications to intelligent vehicles. As PhD candidate,  you will perform cutting edge research in AMLAB in the field of machine learning, will be supervised by Prof dr. Max Welling (promotor). The research topic is ‘Methods for Robust Feature Learning’ where the goals are to learn features that are robust or invariant to changing conditions, to learn classifiers that perform well on multiple domains, to learn representations that can be transferred between domains and to learn classifiers that do not suffer from adversarial examples. Keywords: Bayesian Regularization, Model Uncertainty, Domain Transfer, Representation Learning.

PhD position in Machine Learning and Deep Learning. This position is funded by Bosch Research, is within UvA-Bosch Delta Lab whose research focuses on deep learning and applications to intelligent vehicles. As PhD candidate,  you will perform cutting edge research in AMLAB in the field of machine learning, will be supervised by Prof dr. Arnold Smeulders (promotor). The research topic is ‘Learning to Follow Objects over Multiple Cameras‘ where the goals are to follow objects over multiple camera’s regardless variations in pose, illumination, occlusion, scale and other sources of variance in spite of the fact that most subjects are seen for the first time. This is a hard problem which requires learning generics on many examples and subsequently tailoring it to the current specific case. Keywords: Object Tracking, Generic to specific, Representation learning.

Prof. Darrell is on the faculty of the CS and EE Divisions of the EECS Department at UC Berkeley. He leads Berkeley’s DeepDrive (BDD) Industrial Consortia, is co-Director of the Berkeley Artificial Intelligence Research (BAIR) lab, and is Faculty Director of PATH at UC Berkeley. Darrell’s group develops algorithms for large-scale perceptual learning, including object and activity recognition and detection, for a variety of applications including autonomous vehicles, media search, and multimodal interaction with robots and mobile devices. His areas of interest include computer vision, machine learning, natural language processing, and perception-based human computer interfaces. Prof. Darrell previously led the vision group at the International Computer Science Institute in Berkeley, and was on the faculty of the MIT EECS department from 1999-2008, where he directed the Vision Interface Group. He was a member of the research staff at Interval Research Corporation from 1996-1999, and received the S.M., and PhD. degrees from MIT in 1992 and 1996, respectively. He obtained the B.S.E. degree from the University of Pennsylvania in 1988.

Colloquium – Computational Sensorimotor Learning
Pulkit Agrawal – July 6 2018

Colloquium – Feature Generating Networks for Zero-Shot Learning
Yongqin Xian – May 9 2018

Abstract: Suffering from the extreme training data imbalance between seen and unseen classes,  most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets — CUB, FLO, SUN, AWA and ImageNet — in both the zero-shot learning and generalized zero-shot learning settings.

Yongqin Xian received his B.Sc. degree from Beijing Institute of Technology, China, in 2013 and his M.Sc. degree with honors in computer science from Saarland University, Germany in 2016. He is currently a PhD student with Prof. Bernt Schiele and dr. Zeynep Akata at the Max-Planck Institute for Informatics in Germany. He has served as a reviewer for CVPR 2018, IEEE TPAMI and IEEE TIP. He is a Qualcomm Innovation Fellowship Finalist. His research interests lie in zero-shot learning, few-shot learning, and generative adversarial networks.

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PhD position in Machine Learning and Deep Learning. This position is funded by Bosch Research, is within UvA-Bosch Delta Lab whose research focuses on deep learning and applications to intelligent vehicles. As PhD candidate,  you will perform cutting edge research in AMLAB in the field of machine learning, will be supervised by Prof dr. Max Welling (promotor) and dr. Zeynep Akata (second supervisor). The research topic is ‘Methods for Semi-supervised Learning and Active Labeling’ where the goals are to use both labeled and unlabeled data in training a classifier, to train classifiers when the number of (labeled) examples is small and to detect bad labels and to suggest informative examples to be labeled.

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