October 1, 2020 Machine Learning

Balancing thermal comfort datasets: We GAN, but should we?

Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. These data can be used for modeling comfort behavior to support design and operations towards energy efficiency and well-being… By nature, occupant subjective feedback is imbalanced as indoor conditions are designed for comfort, and responses indicating otherwise are less common. This situation creates a scenario for the machine learning workflow where class balancing as a […]

October 1, 2020 CAUSAL INFERENCE, Machine Learning

Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect

As the class size grows, maintaining a balanced dataset across many classes is challenging because the data are long-tailed in nature; it is even impossible when the sample-of-interest co-exists with each other in one collectable unit, e.g., multiple visual instances in one image. Therefore, long-tailed classification is the key to deep learning at scale… However, existing methods are mainly based on re-weighting/re-sampling heuristics that lack a fundamental theory. In this paper, we establish a causal inference framework, which not only […]

October 1, 2020 FEW-SHOT LEARNING, Machine Learning

Interventional Few-Shot Learning

We uncover an ever-overlooked deficiency in the prevailing Few-Shot Learning (FSL) methods: the pre-trained knowledge is indeed a confounder that limits the performance. This finding is rooted from our causal assumption: a Structural Causal Model (SCM) for the causalities among the pre-trained knowledge, sample features, and labels… Thanks to it, we propose a novel FSL paradigm: Interventional Few-Shot Learning (IFSL). Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards […]

October 1, 2020 Machine Learning, SELF-DRIVING CARS

Artificial Intelligence in Surgery: Neural Networks and Deep Learning

Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such computational methods… However, surgeons and computer scientists should partner to develop and assess deep learning applications of value to patients and healthcare systems. This chapter and the accompanying hands-on material were designed for surgeons willing to understand the intuitions behind neural networks, become familiar with deep […]

October 1, 2020 Machine Learning, QUANTIZATION

Rotated Binary Neural Network

Binary Neural Network (BNN) shows its predominance in reducing the complexity of deep neural networks. However, it suffers severe performance degradation… One of the major impediments is the large quantization error between the full-precision weight vector and its binary vector. Previous works focus on compensating for the norm gap while leaving the angular bias hardly touched. In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary […]

October 1, 2020 Machine Learning, TOPIC MODELS

AOBTM: Adaptive Online Biterm Topic Modeling Method for Version Sensitive Short-texts Analysis

Analysis of mobile app reviews has shown its important role in requirement engineering, software maintenance, and the evolution of mobile apps. Mobile app developers check their users’ reviews frequently to clarify the issues experienced by users or capture the new issues that are introduced due to a recent app update… App reviews have a dynamic nature and their discussed topics change over time. The changes in the topics among collected reviews for different versions of an app can reveal important […]

October 1, 2020 CONTRASTIVE LEARNING, Machine Learning

G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling

In the realms of computer vision, it is evident that deep neural networks perform better in a supervised setting with a large amount of labeled data. The representations learned with supervision are not only of high quality but also helps the model in enhancing its accuracy… However, the collection and annotation of a large dataset are costly and time-consuming. To avoid the same, there has been a lot of research going on in the field of unsupervised visual representation learning […]

October 1, 2020 DIALOGUE UNDERSTANDING, Machine Learning

Utterance-level Dialogue Understanding: An Empirical Study

The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances… In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act […]

October 1, 2020 Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 43: Dynamic Programming Encoding

One of the narratives people (including me) love to associate with neural machine translation is that we got rid of all linguistic assumptions about the text and let the neural network learn their own way independent of what people think about language. It sounds cool, it almost gives a science-fiction feeling. What I think that we really do is that we move our assumptions about language from hard constrains of discrete representation into soft constraints of inductive biases that we […]

October 1, 2020 Artificial intelligence Leave a comment

Archai can design your neural network with state-of-the-art neural architecture search (NAS)

The goal of neural architecture search (NAS) is to have computers automatically search for the best-performing neural networks. Recent advances in NAS methods have made it possible to build problem-specific networks that are faster, more compact, and less power hungry than their handcrafted counterparts. Unfortunately, many NAS methods rely on an array of tricks that aren’t always documented in a way that’s easy to discover. While these tricks result in neural networks with greater accuracy, they often cloud the performance […]

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