Hyunjik Kim

Preprints | Publications

I'm a research scientist at DeepMind at the Google London office, working on probabilistic modelling, disentangling, and models for deep learning that use attention. Prior to that I did my PhD in machine learning at the University of Oxford, supervised by Prof. Yee Whye Teh in the Machine Learning group at the Department of Statistics.

My research interests lie at the intersection of Bayesian Machine Learning and Deep Learning, especially interpretable models that arise in this intersection. In particular, I'm interested in self-attention, unsupervised/semi-supervised representation learning (e.g. disentangling) and learning stochastic processes via Deep Learning methods. Other (related) areas of interest include generative models of image/video and meta-learning. Previously, I have worked on scaling up inference for Gaussian processes, in particular on regression models for collaborative filtering that are motivated by a scalable approximation to a GP, as well as a method for scaling up the compositional kernel search used by the Automatic Statistician via variational sparse GP methods.

Before my PhD, I studied Mathematics at the University of Cambridge, from which I obtained B.A. and M.Math. degrees. I spent a summer at Microsoft Research, Cambridge as a research intern, and worked on collaborative filtering. I spent last summer interning at DeepMind working on unsupervised learning of disentangled representations.

Curriculum Vitae (last updated: June 2019)

Google scholar page

E-mail: hkim@stats.ox.ac.uk


Talk - Attention: the Analogue of Kernels in Deep Learning

Abstract: There have been many recent works that lie at the intersection of kernel methods and deep learning, namely Deep Kernel Learning, Deep Gaussian Processes (GPs) and Convolutional GPs. However such works are often motivated by borrowing ideas that originate from deep learning and incorporating them into kernel methods. In this talk, we will explore the concept of attention or self-attention, that has interestingly travelled the opposite path; it is inherently motivated from kernels, but is being used extensively in state-of-the-art deep learning models in various data modalities. We investigate attention in more detail by studying the  Attentive Neural Process (ANP) that incorporates attention into the recently introduced Neural Process (NP),  a deep models that learns a stochastic process. We show that ANPs address some fundamental drawbacks of NPs by bringing them closer to GPs, while maintaining the benefits of neural networks such as scalability and flexibility.

Venue: Recent Developments in Kernel Methods workshop @Gatsby Computational Neuroscience Unit, UCL, 27/09/19.
Attentive Neural Processes

Abstract: Neural Processes (NPs) (Garnelo et al., 2018a,b) approach regression by learning to map a context set of observed input-output pairs to a distribution over regression functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently with linear complexity in the number of context input-output pairs, and can learn a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a fundamental drawback of underfitting, giving inaccurate predictions at the inputs of the observed data they condition on. We address this issue by incorporating attention into NPs, allowing each input location to attend to the relevant context points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions that can be modelled.

Hyunjik Kim, Andriy Mnih, Jonathan Schwarz, Marta Garnelo, Ali Eslami, Dan Rosenbaum, Oriol Vinyals, Yee Whye Teh
Bayesian Deep Learning Workshop, NeurIPS 2018. Contributed Talk.
ICLR 2019.
pdf | bibtex | openreview | github


Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects

Abstract: We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep generative model for videos of moving objects. It can reliably discover and track objects throughout the sequence of frames, and can also generate future frames conditioning on the current frame, thereby simulating expected motion of objects. This is achieved by explicitly encoding object presence, locations and appearances in the latent variables of the model. SQAIR retains all strengths of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016), including learning in an unsupervised manner, and addresses its shortcomings. We use a moving multi-MNIST dataset to show limitations of AIR in detecting overlapping or partially occluded objects, and show how SQAIR overcomes them by leveraging temporal consistency of objects. Finally, we also apply SQAIR to real-world pedestrian CCTV data, where it learns to reliably detect, track and generate walking pedestrians with no supervision.

Adam Kosiorek, Hyunjik Kim, Ingmar Posner, Yee Whye Teh
NeurIPS 2018, Spotlight.
pdf | bibtex | github
Disentangling by Factorising

Abstract: We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon β-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.

Hyunjik Kim, Andriy Mnih
Learning Disentangled Representations: From Perception to Control Workshop, NIPS 2017. Spotlight Talk.
ICML 2018.
pdf | bibtex
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

Abstract: Automating statistical modelling is a challenging problem in artificial intelligence. The Automatic Statistician takes a first step in this direction, by employing a kernel search algorithm with Gaussian Processes (GP) to provide interpretable statistical models for regression problems. However this does not scale due to its O(N^3) running time for the model selection. We propose Scalable Kernel Composition (SKC), a scalable kernel search algorithm that extends the Automatic Statistician to bigger data sets. In doing so, we derive a cheap upper bound on the GP marginal likelihood that sandwiches the marginal likelihood with the variational lower bound. We show that the upper bound is significantly tighter than the lower bound and thus useful for model selection.

Hyunjik Kim, Yee Whye Teh
AutoML 2016, Journal of Machine Learning Research Workshop and Conference Proceedings.
Practical Bayesian Nonparametrics Workshop, NIPS 2016. Oral & Travel Award.
AISTATS 2018, Oral.
pdf | bibtex


Collaborative Filtering with Side Information: a Gaussian Process Perspective

Abstract: We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

Hyunjik Kim, Xiaoyu Lu, Seth Flaxman, Yee Whye Teh
ArXiv, 2016.
pdf | bibtex


Interpretable Models in Probabilistic Deep Learning

Abstract: As Deep Learning (DL) solutions to real-world problems are becoming increasingly common, DL researchers are striving to better understand the models that they develop. The community has been using the term ‘interpretability’ to describe models and methods that help us achieve this rather vague goal. However many claim that deep models are inherently uninterpretable due to their black-box nature, and stop paying attention to interpretability in deep models on these grounds. In this talk, we show that ‘deep’ and ‘interpretability’ are not mutually exclusive terms, hence it is both possible and necessary to devise interpretable deep models. We first clarify what is meant by the term ‘interpretability’, by listing its desiderata and properties. We then introduce examples of deep probabilistic models that enjoy various properties of interpretability: the talk will cover FactorVAE, a model for learning disentangled representations, and the Attentive Neural Process, a model for learning stochastic processes in a data-driven fashion, focusing on their applications to image data.

Venues: Korea Institute of Science and Technology (KIST), Center for Imaging Media Research, 03/04/19.
Naver Labs, 04/04/19.
Seoul National University, Computer Vision Lab, 05/04/19.

Public Engagement

Introducing Machine Learning to the Public

I helped create a cute two-minute animation that introduces machine learning to the general public, along with friends at Oxford. Check it out below!

Further details can be found here