642 citations · 3.9k across the 143 of their papers we have counts for
15 papers · 1 filter
Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach
Yu Zheng, Bowei Chen, Timothy M. Hospedales +1
Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which cont…
Deep clustering with concrete k-means
Boyan Gao, Yongxin Yang, Henry Gouk +1
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential of deep k-mea…
Interpreting Knowledge Graph Relation Representation from Word Embeddings
Carl Allen, Ivana Balažević, Timothy Hospedales
Many models learn representations of knowledge graph data by exploiting its low-rank latent structure, encoding known relations between entities and enabling unknown facts to be in…
Zero-Shot Crowd Behavior Recognition
Xun Xu, Shaogang Gong, Timothy Hospedales
Understanding crowd behavior in video is challenging for computer vision. There have been increasing attempts on modeling crowded scenes by introducing ever larger property ontolog…
Goal-Driven Sequential Data Abstraction
Umar Riaz Muhammad, Yongxin Yang, Timothy M. Hospedales +2
Automatic data abstraction is an important capability for both benchmarking machine intelligence and supporting summarization applications. In the former one asks whether a machine…
Measuring the Transferability of Adversarial Examples
Deyan Petrov, Timothy M. Hospedales
Adversarial examples are of wide concern due to their impact on the reliability of contemporary machine learning systems. Effective adversarial examples are mostly found via white-…
Factorized Higher-Order CNNs with an Application to Spatio-Temporal Emotion Estimation
Jean Kossaifi, Antoine Toisoul, Adrian Bulat +3
Training deep neural networks with spatio-temporal (i.e., 3D) or multidimensional convolutions of higher-order is computationally challenging due to millions of unknown parameters…
Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
Miguel Jaques, Michael Burke, Timothy Hospedales
We propose a model that is able to perform unsupervised physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known…
Multi-relational Poincaré Graph Embeddings
Ivana Balažević, Carl Allen, Timothy Hospedales
Hyperbolic embeddings have recently gained attention in machine learning due to their ability to represent hierarchical data more accurately and succinctly than their Euclidean ana…
Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
Jieru Jia, Qiuqi Ruan, Timothy M. Hospedales
Contemporary person re-identification (\reid) methods usually require access to data from the deployment camera network during training in order to perform well. This is because co…
Investigating Generalisation in Continuous Deep Reinforcement Learning
Chenyang Zhao, Olivier Sigaud, Freek Stulp +1
Deep Reinforcement Learning has shown great success in a variety of control tasks. However, it is unclear how close we are to the vision of putting Deep RL into practice to solve r…
Episodic Training for Domain Generalization
Da Li, Jianshu Zhang, Yongxin Yang +3
Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training…
Feature-Critic Networks for Heterogeneous Domain Generalization
Yiying Li, Yongxin Yang, Wei Zhou +1
The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques allev…
Analogies Explained: Towards Understanding Word Embeddings
Carl Allen, Timothy Hospedales
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen a…
TuckER: Tensor Factorization for Knowledge Graph Completion
Ivana Balažević, Carl Allen, Timothy M. Hospedales
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferri…