You are all cordially invited to the AMLab seminar on Tuesday May 2 at 16:00 in C3.163, where Zeynep Akata will give a talk titled “Vision and Language for Multimodal Deep Learning”. Afterwards there are the usual drinks and snacks.
Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know well. We tackle the zero-shot learning problem by learning a compatibility function such that matching image-class embedding pairs are assigned a higher score than mismatching pairs; zero-shot classification proceeds by finding the label vector yielding the highest joint compatibility score. We propose and compare different class embeddings learned automatically from unlabeled text corpora and from expert annotated attributes. Attribute annotations performed by humans are not readily available for most classes. On the other hand, humans have a natural ability to determine distinguishing properties of unknown objects. We use detailed visual descriptions collected from naive users as side-information for zero-shot learning, to generate images from scratch and to generate visual explanations which justify a classification decision.
Akata et.al, Label Embeddings for Image Classification, TPAMI 2016
Xian et.al, Latent Embeddings for Zero-Shot Classification, CVPR 2016
Akata et.al, Multi-Cue Zero-Shot Learning with Strong Supervision, CVPR 2016
Xian et.al., Zero-Shot Learning: The Good, the Bad and the Ugly, CVPR’17
Karessli et.al., Gaze Embeddings for Zero-Shot Learning, CVPR’17
Reed et.al, Learning Deep Representations of Fine-Grained Visual Descriptions, CVPR 2016
Reed et.al, Generative Adversarial Text to Image Synthesis, ICML 2016
Reed et.al, Learning What and Where to Draw, NIPS 2016
Hendricks, Akata et.al, Generating Visual Explanations, ECCV 2016
You are all cordially invited to the AMLab seminar on Tuesday April 18 at 16:00 in C3.163, where Philip Versteeg will give a talk titled “Prediction and validation of causal effects in gene knockout experiments”. Afterwards there are the usual drinks and snacks!
Abstract: Causal questions abound across the empirical sciences, including basic biology, epidemiology, psychology and economics. Molecular biology is a particularly interesting area due to the ability in that field to perform interventions via diverse experimental techniques, such as measurements of gene expression levels under single gene knockouts. Such experiments are a key tool for dissecting causal regulatory relationships and provide an opportunity to validate causal discovery methods using real experimental data. In this talk, we provide results of an empirical assessment of several causal discovery algorithms using large-scale data from knockout experiments. We discuss several measures of performance defined using held-out observational and interventional data and find that while discovering system-wide causal structure remains difficult, especially when using only observational data, predicting the set of strongest causal effects is more feasible. We report that predictions of the strongest total causal effects based on a combination of interventional and observational data can be stable across performance measures and consistently outperform non-causal baselines.
You are all cordially invited to the AMLab seminar on Tuesday April 11 at 16:00 in C3.163, where Rianne van den Berg will give a talk titled “Graph convolutional networks as recommender systems”. Afterwards there are the usual drinks and snacks!
Abstract: Graph convolutional neural networks as introduced by Thomas Kipf and Max Welling have been used for link prediction and entity classification in undirected graphs, and more recently also in multi-relational directed graphs. In this talk I will present preliminary results on the application of relational graph convolutional networks to link prediction in recommender systems. I will discuss the possibility to merge interaction data between users and items with knowledge graphs and social networks, and show preliminary results on how this affects the performance in rating predictions.
You are all cordially invited to the AMLab seminar on Tuesday April 4 at 16:00 in C3.163, where Tineke Blom will give a talk titled “Causal Discovery in the Presence of Measurement Error”. Afterwards there are the usual drinks and snacks!
Abstract: Causal discovery algorithms can predict causal relationships based on several assumptions, which include the absence of measurement error. However, this assumption is most likely violated in practical applications, resulting in erroneous, irreproducible results. In this work, we examine the effect of different types of measurement error in a linear model of three variables, which is a minimal example of an identifiable causal relationship. We show that the presence of unknown measurement error makes it impossible to detect independences between the actual variables from the data using regular statistical testing and conventional thresholds for (in)dependence. We show that for limited measurement error, we can obtain consistent causal predictions by allowing for a small amount of dependence between (conditionally) independent variables. We illustrate our results in both simulated and real world protein-signaling data.