Institute Talks

IS Colloquium

- 25 September 2017 • 11:15 12:15

- Amos Storkey

- Tübingen, MPI_IS Lecture Hall (ground floor)

In this talk I introduce the neural statistician as an approach for meta learning. The neural statistician learns to appropriately summarise datasets through a learnt statistic vector. This can be used for few shot learning, by computing the statistic vectors for the presented data, and using these statistics as context variables for one-shot classification and generation. I will show how we can generalise the neural statistician to a context aware learner that learns to characterise and combine independently learnt contexts. I will also demonstrate an approach for meta-learning data augmentation strategies. Acknowledgments: This work is joint work with Harri Edwards, Antreas Antoniou, and Conor Durkan.

Organizers: Philipp Hennig

Archived Talks

- Sven Dickinson

- Green-House (PS)

Human observers can classify photographs of real-world scenes after only a very brief exposure to the image (Potter & Levy, 1969; Thorpe, Fize, Marlot, et al., 1996; VanRullen & Thorpe, 2001). Line drawings of natural scenes have been shown to capture essential structural information required for successful scene categorization (Walther et al., 2011). Here, we investigate how the spatial relationships between lines and line segments in the line drawings affect scene classification. In one experiment, we tested the effect of removing either the junctions or the middle segments between junctions. Surprisingly, participants performed better when shown the middle segments (47.5%) than when shown the junctions (42.2%). It appeared as if the images with middle segments tended to maintain the most parallel/locally symmetric portions of the contours. In order to test this hypothesis, in a second experiment, we either removed the most symmetric half of the contour pixels or the least symmetric half of the contour pixels using a novel method of measuring the local symmetry of each contour pixel in the image. Participants were much better at categorizing images containing the most symmetric contour pixels (49.7%) than the least symmetric (38.2%). Thus, results from both experiments demonstrate that local contour symmetry is a crucial organizing principle in complex real-world scenes. Joint work with John Wilder (UofT CS, Psych), Morteza Rezanejad (McGill CS), Kaleem Siddiqi (McGill CS), Allan Jepson (UofT CS), and Dirk Bernhardt-Walther (UofT Psych), to be presented at VSS 2017.

Organizers: Ahmed Osman

IS Colloquium

- 29 May 2017 • 12:00 13:00

- Sebastian Nowozin

- Max Planck House Lecture Hall

Probabilistic deep learning methods have recently made great progress for generative and discriminative modeling. I will give a brief overview of recent developments and then present two contributions. The first is on a generalization of generative adversarial networks (GAN), extending their use considerably. GANs can be shown to approximately minimize the Jensen-Shannon divergence between two distributions, the true sampling distribution and the model distribution. We extend GANs to the class of f-divergences which include popular divergences such as the Kullback-Leibler divergence. This enables applications to variational inference and likelihood-free maximum likelihood, as well as enables GAN models to become basic building blocks in larger models. The second contribution is to consider representation learning using variational autoencoder models. To make learned representations of data useful we need ground them in semantic concepts. We propose a generative model that can decompose an observation into multiple separate latent factors, each of which represents a separate concept. Such disentangled representation is useful for recognition and for precise control in generative modeling. We learn our representations using weak supervision in the form of groups of observations where all samples within a group share the same value in a given latent factor. To make such learning feasible we generalize recent methods for amortized probabilistic inference to the dependent case. Joint work with: Ryota Tomioka (MSR Cambridge), Botond Cseke (MSR Cambridge), Diane Bouchacourt (Oxford)

Organizers: Lars Mescheder

- Yael Moses

- Greenhouse (PS)

Dynamic events such as family gatherings, concerts or sports events are often photographed by a group of people. The set of still images obtained this way is rich in dynamic content. We consider the question of whether such a set of still images, rather the traditional video sequences, can be used for analyzing the dynamic content of the scene. This talk will describe several instances of this problem, their solutions and directions for future studies. In particular, we will present a method to extend epipolar geometry to predict location of a moving feature in CrowdCam images. The method assumes that the temporal order of the set of images, namely photo-sequencing, is given. We will briefly describe our method to compute photo-sequencing using geometric considerations and rank aggregation. We will also present a method for identifying the moving regions in a scene, which is a basic component in dynamic scene analysis. Finally, we will consider a new vision of developing collaborative CrowdCam, and a first step toward this goal.

Organizers: Jonas Wulff

- Guido Montúfar

- N4.022 (EI Dept. meeting room / 4th floor, north building)

Deep Learning is one of the most successful machine learning approaches to artificial intelligence. In this talk I discuss the geometry of neural networks as a way to study the success of Deep Learning at a mathematical level and to develop a theoretical basis for making further advances, especially in situations with limited amounts of data and challenging problems in reinforcement learning. I present a few recent results on the representational power of neural networks and then demonstrate how to align this with structures from perception-action problems in order to obtain more efficient learning systems.

Organizers: Jane Walters

- Dino Sejdinovic

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting distance between distributions, are useful tools for fully nonparametric hypothesis testing and for learning on distributional inputs. I will give an overview of this framework and present some of its recent applications within the context of approximate Bayesian inference. Further, I will discuss a recent modification of MMD which aims to encode invariance to additive symmetric noise and leads to learning on distributions robust to the distributional covariate shift, e.g. where measurement noise on the training data differs from that on the testing data.

Organizers: Philipp Hennig

- Cordelia Schmid

- Greenhouse (PS)

This talk addresses the task of segmenting moving objects in unconstrained videos. We introduce a novel two-stream neural network with an explicit memory module to achieve this. The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. Given video frames as input, our approach first assigns each pixel an object or background label obtained with an encoder-decoder network that takes as input optical flow and is trained on synthetic data. Next, a “visual memory” specific to the video is acquired automatically without any manually-annotated frames. The visual memory is implemented with convolutional gated recurrent units, which allows to propagate spatial information over time. We evaluate our method extensively on two benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show state-of-the-art results. This is joint work with K. Alahari and P. Tokmakov.

Organizers: Osman Ulusoy

Talk

- 19 May 2017 • 11:00 12:15

- Dr. Raj Madhavan

- N2.025 (AMD seminar room - 2nd floor)

Many of the existing Robotics & Automation (R&A) technologies are at a sufficient level of maturity and are widely accepted by the academic (and to a lesser extent by the industrial) community after having undergone the scientific rigor and peer reviews that accompany such works. I believe that most of the past and current research and development efforts in robotics and automation have been squarely aimed at increasing the Standard of Living (SoL) in developed economies where housing, running water, transportation, schools, access to healthcare, to name a few, are taken for granted. Humanitarian R&A, on the other hand, can be taken to mean technologies that can make a fundamental difference in people’s lives by alleviating their suffering in times of need, such as during natural or man-made disasters or in pockets of the population where the most basic needs of humanity are not met, thus improving their Quality of Life (QoL) and not just SoL. My current work focuses on the applied use of robotics and automation technologies for the benefit of under-served and under-developed communities by working closely with them to develop solutions that showcase the effectiveness of R&A solutions in domains that strike a chord with the beneficiaries. This is made possible by bringing together researchers, practitioners from industry, academia, local governments, and various entities such as the IEEE Robotics Automation Society’s Special Interest Group on Humanitarian Technology (RAS-SIGHT), NGOs, and NPOs across the globe. I will share some of my efforts and thoughts on challenges that need to be taken into consideration including sustainability of developed solutions. I will also outline my recent efforts in the technology and public policy domains with emphasis on socio-economic, cultural, privacy, and security issues in developing and developed economies.

Organizers: Ludovic Righetti

- Carolin Schmitt

- PS Green House

I'll present my master thesis "Biquadratic Forms and Semi-Definite Relaxations". It is about biquadratic optimization programs (which are NP-hard generally) and examines a condition under which there exists an algorithm that finds a solution to every instance of the problem in polynomial time. I'll present a counterexample for which this is not possible generally and face the question of what happens if further knowledge about the variables over which we optimise is applied.

Organizers: Fatma Güney

- Björn Andres

- N3.022

A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. This talk is about optimization problems whose feasible solutions define decompositions of a graph. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows applications of this problem and proposed generalizations to diverse image analysis tasks. It sketches algorithms for finding feasible solutions for large instances in practice, solutions that are often superior in the metrics of application-specific benchmarks. It also sketches algorithms for finding lower bounds and points to new findings and open problems of polyhedral geometry in this context.

Organizers: Christoph Lassner

Talk

- 05 May 2017 • 14:00 14:50

- Rahul Chaudhari and David Gueorguiev

- N2.025

Colloquium on haptics: Two guests of the department "Haptic Intelligence" (Dept. Kuchenbecker), will each give a short talk this Friday (May 5) in Tübingen. The talks will be broadcasted to Stuttgart, room 2 P4.