QUVA Colloquium

The goal of the Qualcomm-UvA Deep Vision Seminars is to invite seminal guest speakers to provide talks on the latest advances in the areas of Deep Learning, Computer Vision, and Machine Learning.

Next Seminar: Friday January 27, 2017 11.00-13.00 hrs room C0.110. Speaker: t.b.a


December 2nd 2016 – Invited Talk by Jason Yosinksi from Geometric Intelligence.
Title: A deeper understanding of large neural nets
Deep neural networks have recently been making a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but hard for machines, like playing Atari games or identifying lions or jaguars in photos. But how do these neural nets actually work? What do they learn? This turns out to be a surprisingly tricky question to answer — surprising because we built the networks, but tricky because they are so large and have many millions of connections that effect complex and hard to interpret computation. Trickiness notwithstanding, in this talk we’ll see what we can learn about neural nets by looking at a few examples of networks in action and experiments designed to elucidate network behavior. The combined experiments yield a better understanding of network behavior and capabilities and promise to bolster our ability to apply neural nets as components in real world computer vision systems.

October 28th 2016 – Invited Talk by Max Jaderberg from Google DeepMind.
Title: Temporal Credit Assignment for Training Recurrent Neural Networks
The problem of temporal credit assignment is at the heart of training temporal models — how the processing or actions performed in the past affects the future, and how we can train this processing to optimise future performance. This talk will focus on two distinct scenarios. First the reinforcement learning scenario, where we consider an agent which is a recurrent neural network which takes actions in its environment. I will show our state-of-the-art approach to deep reinforcement learning, and some of the latest methods which deal with enhancing temporal credit assignment, presenting results on new 3D environments. I will then look at how temporal credit assignment is performed more generically during the training of recurrent neural networks, and how this can be improved by the introduction of Synthetic Gradients — predicted gradients from future processing by local models learnt online.

September 29th 2016 – Invited Talk by Iasonas Kokkinos from INRIA.
Title: DeepLab to UberNet: From Task-specific to Task-agnostic Deep Learning
Over the last few years Convolutional Neural Networks (CNNs) have been shown to deliver excellent results in a broad range of low- and high-level vision tasks, spanning effectively the whole spectrum of computer vision problems.
In this talk we will present recent research progress along two complementary directions. In the first part we will present research efforts on integrating established computer vision ideas with CNNs, thereby allowing us to incorporate task-specific domain knowledge in CNNs. We will present CNN-based adaptations of structured prediction techniques that use discrete (DenseCRF – Deeplab) and continuous energy-based formulations (Deep Gaussian CRF), and will also present methods to incorporate ideas from multi-scale processing, Multiple-Instance Learning and Spectral Clustering into CNNs. In the second part of the talk we will turn to designing a generic architecture that can tackle a multitude of tasks jointly, aiming at designing a `swiss knife’ for vision. We call this network an ‘UberNet’ to underline its overarching nature. We will introduce techniques that allow us to train an UberNet while using datasets with diverse annotations, while also handling the memory limitations of current hardware. The proposed architecture is able to jointly address (a) boundary detection (b) saliency detection (c) normal estimation (d) semantic segmentation (e) human part segmentation (f) human boundary detection (g) region proposal generation and object detection in 0.7 seconds per frame, with a level of performance that is comparable to the current state-of-the-art on these tasks.