Brain-inspired computing

Understanding the inner workings of the brain unveils a tremendous potential to advance AI. The research in neuromorphic computing takes inspiration from the brain to develop novel computing paradigms. In particular, we will focus on three key brain-inspired aspects of neuromorphic computing. Firstly, the physical structure of the nervous tissue that leads to the concept of mixed digital-analog in-memory AI accelerators and offers speed and efficiency advantages over GPUs. Secondly, the tempora ...

Stanisław Woźniak pursues his research in the field of neuromorphic computing, taking inspiration from the neural networks in the brain to develop novel computing architectures. He received his Ph.D. in 2017 from École Polytechnique Fédérale de Lausanne. In 2018, he received the Fritz Kutter Award of ETH Zurich and IBM Research Division Award for contributions in neuromorphic computing using phase-change memory devices. Since 2018, he is a Post-doctoral Researcher at IBM Research – Zurich. His current focus is on closing the gap between the theoretically appealing properties of Spiking Neural Networks and their applications.

On Deep Learning of Sets

Deep learning is traditionally concerned with ordered data such as images (ordered pixels) or natural language sequences (ordered tokens). In many cases, like multiple-object tracking or epidemiological modelling, there is no such order, and data are best treated as sets. In this talk, we will consider sets and how their lack of structure influences deep learning model design. To this end, we will divide set learning into three problems: set to vector, vector to set, and set to set. We will expl ...

Adam R. Kosiorek is a Research Scientist at Google DeepMind in London. His research interests include unsupervised representation learning, particularly learning object-centric representations from images and videos without supervision, and generative modelling. He is also interested in inference methods for probabilistic generative models. Previously, Adam interned at Google Brain in Toronto with Geoff Hinton, where he developed Stacked Capsule Autoencoders. He received his Ph.D. from the University of Oxford, MSc from the Technical University of Munich and BSc from the Warsaw University of Technology. In his free time, Adam likes reading books and training calisthenics.

The 7 Habits of Highly Effective ML Practitioners

Machine Learning is an extremely powerful toolkit for quickly building complex systems that can learn from data. However, there’s no free lunch - with the great power of those methods comes great complexity, in terms of development, deployment and maintenance. In this talk I’m going to discuss 7 habits that can vastly improve the odds of success of the ML system, based on my personal experience from places like Gmail Intelligence and Google Brain. We will start by explaining how to ap ...

Karol Kurach is a Chief Technology Officer at Cosmose, trying to understand and predict how 1 billion people shop offline. Before Cosmose, he was a Research Lead at Google Brain, focusing on projects related to Reinforcement Learning, GANs and sequential models. He is also a co-author of intelligence systems inside Gmail, such as Smart Reply and Categories (a.k.a. Tabs). During his PhD at the University of Warsaw, he worked on deep neural architectures with external memory, in collaboration with researchers from Brain and OpenAI. Winner of many programming competitions, including 24h coding marathons. He represented Poland and was an European Champion & ACM ICPC World Finals silver medalist.

Delivering FastPitch: A Major League, Parallel Text-to-speech Model

FastPitch is a recently-developed TTS model based on FastSpeech. It seems to be a home run: with mel-spectrogram synthesis up to 1000x real time, favorable MOS scores, a high degree of control by conditioning on pitch contours or speaker identity, trainable on commodity hardware. Right off the bat we will focus on the development process, rather than the model. We will look for clues in the original FastSpeech paper and try our luck re-implementing. Then, we will discuss a practical strategy ...

Adrian Łańcucki is a Senior Deep Learning Engineer at NVIDIA Poland. He received his PhD in Machine Learning in 2019 from University of Wrocław. His research interests include NLP, most recently text-to-speech, speech recognition, and representation learning. In 2017 his team ranked 1st ex aequo at the NIPS 2017 Conversational Intelligence Challenge. Briefly visited at NYU, did a research internship at Microsoft, participated in the JSALT summer workshop.

Explainable deep neural networks for medical image analysis

Although deep neural networks have already achieved a good performance in many medical image analysis tasks, their clinical implementation is slower than many anticipated a few years ago. One of the critical issues that remains outstanding is the lack of explainability of the commonly used network architectures imported from computer vision. In my talk, I will explain how we created a new deep neural network architecture, tailored to medical image analysis, in which making a prediction is ins...

Krzysztof is an assistant professor at NYU School of Medicine and an affiliated faculty at NYU Center for Data Science. His main interests are in unsupervised learning with neural networks, model compression, transfer learning, evaluation of machine learning models and applications of these techniques to medical imaging. He previously did a postdoc at NYU with Kyunghyun Cho, a PhD at the University of Edinburgh with Charles Sutton and an MSc as a visiting student at the University of Edinburgh with Amos Storkey. His BSc is from the University of Warsaw. He also did industrial internships in Microsoft Research (Redmond, working with Rich Caruana and Abdel-rahman Mohamed), Amazon (Berlin, Ralf Herbrich's group), Microsoft (Bellevue) and J.P. Morgan (London).

Recent advancements in adversarial speech synthesis

Generative Adversarial Networks have seen rapid development in recent years, leading to major improvements in image and video generation. On the other hand, for a long time successful application of GANs to other domains, in particular audio and speech synthesis, remained a major challenge. During last year, however, several promising approaches appeared, proving that high fidelity speech synthesis is feasible with adversarial nets, and leading to new kinds of efficient, feed-forward generato ...

Mikołaj Bińkowski is a Research Scientist at DeepMind and a PhD student at Imperial College London. His research has focused mostly on various aspects of Generative Adversarial Networks, including training objectives, evaluation and their application to new domains such as speech synthesis. Previously, he interned at Google Brain Amsterdam and at Montreal Institute for Learning Algorithms, where he worked with Aaron Courville on image-to-image transfer. He holds an MSc in Financial Mathematics from the University of Warsaw, and a BSc in Mathematics from the Jagiellonian University.

Transforming Data into Decisions

The talk would cover a few AI-based business applications for transforming data into decisions, based on work done for three companies (NuTech Solutions, SolveIT Software, and Complexica) over the last 20 years. A few general concepts (Adaptive Business Intelligence, Travelling Thief Problem, Larry – the Digital Analyst) would be discussed and illustrated by a few examples. The final part of the talk would present Complexica’s approach for increasing revenue, margin, and customer engageme ...

Zbigniew Michalewicz is currently Emeritus Professor of Computer Science at the University of Adelaide, Australia, and a Professor with the Institute of Computer Science, Polish Academy of Sciences and the Polish-Japanese Institute of Information Technology, Warsaw. He is also the Chief Scientific Officer at Complexica, a leading provider of software applications that harness the power of Artificial Intelligence and big data to optimise sales, marketing, & supply chain decisions. For many years his research interests were in the field of evolutionary computation. He published several books and 300 widely cited technical papers in journals and conference proceedings (over 50,000 citations). Zbigniew has secured numerous multi-million dollar industry contracts from companies such as General Motors, Ford Motor Company, Bank of America, U.S. Department of Defense, PKN Orlen, BB&T, and Dentsu, and his achievements have been recognized by many invited talks publications, including TIME Magazine, Newsweek, New York Times, Forbes, Business Journal, and the Associated Press among others.

Let the Product Lead: R&D Paradigms for Recognition Models of Handwritten Math Expressions

Typical approaches to handwritten recognition tasks involve collecting and tagging large amounts of data on which many iterations of models are trained. The “one dataset, many models” paradigm has specific drawbacks within the context of agile product development. As product requirements evolve naturally, such as the addition of a new characters into the prediction space, a new data collection and tagging effort must be undertaken. In this talk, I’ll describe a different approach where ...

Quinn Lathrop leads a data science team within AI Products and Solutions at Pearson. His team has designed, developed, and deployed a broad set of product features including the handwriting recognition capabilities, reinforcement learning recommenders, and adaptive personalized tutoring systems of the Aida Calculus app. Quinn holds a PhD in Quantitative Psychology from the University of Notre Dame and currently lives in Denver, Colorado.

Challenges of commercializing language models on mobile devices

Language models have been a hot topic in Natural Language Processing for the past few years. Models such as BERT or GPT learn the statistical structure of the language, allowing for unsupervised pre-training for various downstream NLP tasks. But language models are used not only in cutting-edge research. They are also embedded in applications we use every day, such as predictive keyboards on mobile devices. We will learn about language modeling for mobile keyboards, from statistical n-gram model ...

Szymon has over 4 years of experience in the industry of Machine Learning and over 13 years of experience as a scientific researcher. He holds PhD in physics and habilitation (DSc) in computational biology. In 2016 he joined Samsung Electronics as Technical Leader for NLP projects and after 2 years was promoted to the role of Group Leader in the field of Natural Language Processing. He supervised teams focused on NLP and data acquisition. After 4 years of working for Samsung, he joined Allegro as Senior Research Engineer. In his free time he plays Hearthstone.