You are all cordially invited to the AMLab seminar on Tuesday April 17 at 16:00 in C3.163, where Tineke Blom will give a talk titled “Causal Modeling for Dynamical Systems using Generalized Structural Causal Models”. Afterwards there are the usual drinks and snacks!
Abstract: Structural causal models (SCMs) are a popular tool to describe causal relations in systems in many fields such as economy, the social sciences, and biology. Complex (cyclical) dynamical systems, such as chemical reaction networks, are often described by a set of ODEs. We show that SCMs are not flexible enough in general to give a complete causal representation of equilibrium states in these dynamical systems. Since such systems do form an important modeling class for real-world data, we extend the concept of an SCM to a generalized structural causal model. We show that this allows us to capture the essential causal semantics that characterize dynamical systems. We illustrate our approach on a basic enzymatic reaction.
Next week Monday and Tuesday, the AMLab seminar will host two talks at FNWI, Amsterdam Science Park:
On Monday April 9 at 16:00 in room C1.112, Avital Oliver (Google Brain) will give a talk titled “Realistic Evaluation of Semi-Supervised Learning Algorithms“;
On Tuesday April 10 at 16:00 in room F1.02, Petar Veličković (University of Cambridge) will give a talk titled “Keeping our graphs attentive“.
Abstracts and bio’s are included below. Afterwards there will be the usual drinks and snacks. (Note that room F1.02 for Petar’s talk is a several minute walk away from the main entrance.)
You are all cordially invited to the AMLab seminar on Tuesday April 3 at 16:00 in C3.163, where Karen Ullrich will give a talk titled “Variational Bayes Wake-Sleep algorithm for expressive latent representations in 3D protein reconstruction”. Afterwards there are the usual drinks and snacks!
Abstract: Reconstructing three dimensional structures from noisy two dimensional orthographic projections is a central task in many scientific domains, examples range from medical tomography to single particle electron microscopy.
We treat this problem from a Bayesian point of view. Specifically, we regard a specimen’s structure and its pose as latent factors which are marginalized over. This allows us to express uncertainty in pose and even local uncertainty in the sample’s structure. This information can serve to detect unstable sub-structures or multiple configurations of a specimen. In particular, we apply amortized deep neural networks to encode observations into latent factors. This bears the advantage of transferability across multiple structures. To this end, we propose to train the model alternately in observation space and latent space, resulting in a generalized version of the wake-sleep algorithm.
We focus our experiments on cryogenic electron microscopy (CryoEM) single particle analysis, a technique that enables deep understanding of structural biology and chemistry by inspecting single proteins. We show our model to be competitive while predicting reasonable uncertainties. Moreover, we empirically demonstrate that the model is more data efficient than competitive methods and that it is transferable between molecules.
You are all cordially invited to the AMLab seminar on Tuesday March 27 at 16:00 in C3.163, where Wouter Kool will give a talk titled “Attention Solves Your TSP”. Afterwards there are the usual drinks and snacks!
Abstract: We propose a framework for solving combinatorial optimization problems of which the output can be represented as a sequence of input elements. As an alternative to the Pointer Network, we parameterize a policy by a model based entirely on (graph) attention layers, and train it efficiently using REINFORCE with a simple and robust baseline based on a deterministic (greedy) rollout of the best policy found during training. We significantly improve over state-of-the-art results for learning algorithms for the 2D Euclidean TSP, reducing the optimality gap for a single tour construction by more than 75% (to 0.33%) and 50% (to 2.28%) for instances with 20 and 50 nodes respectively.
You are all cordially invited to the AMLab seminar on Tuesday March 20 at 16:00 in C3.163, where Paul Baireuther (Lorentz Institute of Leiden University) will give a talk titled “Quantum Error Correction with Recurrent Neural Networks”. Afterwards there are the usual drinks and snacks!
Abstract: In quantum computation one of the key challenges is to build fault-tolerant logical qubits. A logical qubit consists of several physical qubits. In stabilizer codes, a popular class of quantum error correction schemes, a part of the system of physical qubits is measured repeatedly, without measuring (and collapsing by the Born rule) the state of the encoded logical qubit. These repetitive measurements are called syndrome measurements, and must be interpreted by a classical decoder in order to determine what errors occurred on the underlying physical system. The decoding of these space- and time-correlated syndromes is a highly non-trivial task, and efficient decoding algorithms are known only for a few stabilizer codes. In this talk I will explain how we design and train decoders based on recurrent neural networks.
You are all cordially invited to the AMLab seminar on Tuesday March 13 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where prof. Max Welling will give a talk titled “Stochastic Deep Learning”. Afterwards there are the usual drinks and snacks.
Abstract: Deep learning has been very successful in many applications, but there are a number challenges that still need to be addressed:
1) DL does not provide reliable confidence intervals
2) DL is susceptible to small adversarial input perturbations
3) DL easily overfits
4) DL uses too much energy and memory
In this talk I will argue that we should be looking at stochastic DL models where the hidden units are noisy. We can train these models with variational methods.
A number of interesting connections emerge in such models:
1) The noisy hidden units form an information bottleneck
2) Through local reparameterization we can interpret these models as Bayesian
3) The noise can be used to create privacy preserving models
4) Stochastic quantization to low bit-width can make DL more power and memory efficient.
This talk will not go in great depth in these topics but rather paint the larger picture.
You are all cordially invited to the AMLab seminar on Tuesday March 6 at 16:00 in C3.163, where Thijs van Ommen will give a talk titled “Accurate and efficient causal discovery”. Afterwards there are the usual drinks and snacks!
Abstract: Will administering a certain chemical cause a cancer cell to stop multiplying? To answer this and other scientific “what-if” questions, we need causal models, which describe the cause-effect relations within a system of interest. Because even domain experts may not know the right causal model, we want to learn it automatically from large-scale data. This problem is called causal discovery, and is very difficult: the signals in the data that allow us to distinguish different causal models are often weak, so we need to be careful when interpreting them. Also, the number of candidate models that must be considered makes this problem computationally challenging. I will present some of my recent results which are an important step towards developing a statistically accurate and computationally efficient algorithm for causal discovery.
You are all cordially invited to the AMLab seminar on Tuesday February 20 at 16:00 in C3.163, where Bas Veeling will give a talk titled “Uncertainty in Deep Neural Networks with Stochastic Quantized Activation Variational Inference”. Afterwards there are the usual drinks and snacks!
Abstract: The successful uptake of deep neural networks in high-risk domains is contingent on the capability to ensure minimal-risk guarantees. This requires that deep neural networks provide predictive uncertainty of high quality. Amortized variational inference provides a promising direction to achieve this, but demands a flexible yet tractable approximative posterior, which is an open area of research. We propose “SQUAVI”, a novel and flexible variational inference model that imposes a multinomial distribution on quantized latent variables. The proposed method is scalable, self-normalizing and sample efficient, and we demonstrate that the model utilizes the flexible posterior to its full potential, learns interesting non-linearities, and provides predictive uncertainty of competitive quality.
You are all cordially invited to the AMLab seminar on Tuesday February 13 at 16:00 in C3.163, where ChangYong Oh will give a talk titled “BOCK: Bayesian Optimization with Cylindrical Kernels”. Afterwards there are the usual drinks and snacks!
Abstract: A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space. In this paper we propose BOCK, Bayesian Optimization with Cylindrical Kernels, whose basic idea is to transform the ball geometry of the search space using a cylindrical transformation. Because of the transformed geometry, the Gaussian Process-based surrogate model spends less budget searching near the boundary, while concentrating its efforts relatively more near the center of the search region, where we expect the solution to be located. We evaluate BOCK extensively, showing that it is not only more accurate and efficient, but it also scales successfully to problems with a dimensionality as high as 500. We show that the better accuracy and scalability of BOCK even allows optimizing modestly sized neural network layers, as well as neural network hyperparameters.
You are all cordially invited to the AMLab seminar on Tuesday January 30 at 16:00 in C3.163, where Jorn Peters will give a talk titled “Binary Neural Networks: an overview”. Afterwards there are the usual drinks and snacks!
Abstract: One limiting factor for deploying neural networks in real-world applications (e.g., self-driving cars or smart home appliances) is the requirement for memory, computation and power. As a consequence, it is often infeasible to employ many of today’s deep learning innovations in situations where resources are scarce. One way to combat these resource requirements of neural networks is to reduce the floating point bit-precision for the parameters and/or activations in the neural network, which effectively increases FLOPS and reduces memory requirements. Taking this to the extreme, one obtains binary neural networks, i.e., neural networks in which the parameters and/or activations are constrained to only two possible values (e.g., -1 or 1). In recent years, several methods for training binary neural networks using gradient descent have been developed. In this talk I will give an overview of (a selection) of these methods.