AI Reading Group on Sep 30 2021: Generative Spoken Language Modeling from Raw Audio

Where and when: Thursday, Sep 30 at 2-3pm in Google Meet (see the calendar invite for the link) Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no...

Machine Learning Seminar by Mengjie Zhang – Evolutionary Machine Learning: Research, Applications and Challenges

  Short Bio Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, an IEEE Distinguished Lecturer, currently Professor of Computer Science at Victoria University of Wellington, where he heads the...

ML Student Seminar on Sep 23 2021: Machine learning application in thin-walled structures

Where and when: Thursday, Sep 23 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Arthur (Zhiyuan) Fang, supervised by Dr. Krishanu Roy and Dr. James Lim in the Department of Civil and Environmental Engineering Abstract: Structural...

ML Student Seminar on Sep 9 2021: Machine Learning in Julia

Where and when: Thursday, Sep 9 at 2-3pm in 303S-561 Speaker: Anthony Blaom, https://ablaom.github.io Abstract: Julia is a young but mature programming language which allows one to write fast code fast. Over a two year period, we have developed a Julia toolbox, called...

AI Reading Group on Sep 2 2021: Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Where and when: Thursday, Sep 2 at 2-3pm in 303S-561 Abstract Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed...

ML Student Seminar on Aug 26 2021: Hitting the Target: Stopping Criteria for Active Learning

Where and when: Thursday, Aug 26 at 2-3pm in 303S-561 Speaker: Zac Pullar-Strecker, B.Sc. Student and Research Assistant (for Joerg Wicker) Abstract: Training modern ML models frequently requires large datasets which are expensive and time-consuming to collect. Active...

AI Reading Group on Aug 19 2021: Tabular Data: Deep Learning is Not All You Need

Where and when: Thursday, Aug 19 at 2-3pm in 303S-561 Abstract A key element of AutoML systems is setting the types of models that will be used for each type of task. For classification and regression problems with tabular data, the use of tree ensemble models (like...

ML Student Seminar on Aug 12 2021: Reinforcement Learning Control for Vapour Compression Refrigeration System

Where and when: Thursday, Aug 12 at 2-3pm in 303S-561 Speaker: Tech Logg Ding, Ph.D. student, Department of Mechanical Engineering Abstract: Refrigeration systems are essential for various cooling applications in modern society. The majority of the refrigeration...

AI Reading Group on Aug 5 2021: Compositional Processing Emerges in Neural Networks Solving Math Problems

Where and when: Thursday, Aug 5 at 2-3pm in 303S-561 Abstract A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition. Humans can infer the structured relationships (e.g., grammatical rules) implicit...

ML Student Seminar on July 29 2021: SymbioLCD: Ensemble-Based Loop Closure Detection using CNN-Extracted Objects and Visual Bag-of-Words

Where and when: Thursday, July 29 at 2-3pm in 303S-561 Speaker: Jonathan Kim, PhD student, supervised by Pat Riddle and Joerg Wicker Abstract: Loop closure detection is an essential tool of Simultaneous Localization and Mapping (SLAM) to minimize drift in its...

Adversarial Learning: Robust and Reliable Machine Learning Models

Kia Ora! I am Luke Chang and I am passionate about building more reliable machine learning models, an artificial intelligence people can trust. I started my machine learning journey by designing a transmission controller for an engine in my third-year undergraduate project. I was fascinated by how artificial intelligence can be integrated into everyday systems. When I learnt a state-of-the-art machine learning model is vulnerable to malicious attacks, even when trained on a large amount of...

Knowledge-Driven Text Generation

I am Beryl Qi and my research topic is ‘Knowledge-Driven Text Generation’. Natural language generation (NLG) is an important research direction in the field of natural language processing. This technology can be applied to various information processing tasks, such as QA (Question Answering), IE (Information Extraction), PD (Problem Decomposition), etc. The wide application of NLG benefits from its learning and processing capabilities when facing to explosion of data. The overall system we...

Identification and Mitigation of Selection Bias

Kia Ora! I am Katharina Dost, a PhD student in my second year with the School of Computer Science. My research topic is “Identification and Mitigation of Selection Bias” and I would like to use this post to talk about my research and my experiences, so read on! Our world runs on data. We gather whatever we can and use it to answer a variety of questions. These can be something as simple as “What is the average age of my customers?” but also as critical as “Which treatment is best suited for...

University of Auckland Machine Learning Group

Machine Learning is a field of Computer Science that aims to develop algorithms that learn from experience. This is in contrast to traditional computer science algorithms where behavior is directly coded into the algorithms. Machine Learning algorithms take experience in form of data and observe patterns in these data to make decisions and predictions. Machine Learning became widely popular in recent years and Machine Learning methods are widely used, having applications in every industry...