Machine Learning

TPOT for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global search procedure to efficiently discover a top-performing model pipeline for a given dataset. In this tutorial, you will discover how to use TPOT for AutoML […]

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NLP

A Simple Guide On Using BERT for Binary Text Classification.

Please consider using the Simple Transformers library as it is easy to use, feature-packed, and regularly updated. The article still stands as a reference to BERT models and is likely to be helpful with understanding how BERT works. However, Simple Transformers offers a lot more features, much more straightforward tuning options, all the while being quick and easy to use! The links below should help you get started quickly. Binary Classification

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Leveraging Monolingual Data, NLP

Issue #101 – Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

02 Oct20 Issue #101 – Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Multilingual Neural Machine Translation (NMT), which enables zero-shot MT, is a significant development since the start of NMT. On the one hand, we have evidence that models trained with multiple languages can outperform those trained on a bilingual basis. On the other hand, multilingual NMT also enables us to train models of a language pair […]

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3D OBJECT RECONSTRUCTION, Machine Learning

Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity

Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes… Existing 3D reconstruction techniques optimize for visual reconstruction fidelity, typically measured by chamfer distance or voxel IOU. We find that when applied to realistic, cluttered robotics environments, these systems produce reconstructions with low physical realism, resulting in poor task performance when used for model-based control. We propose ARM, an […]

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Machine Learning

NITI: Training Integer Neural Networks Using Integer-only Arithmetic

While integer arithmetic has been widely adopted for improved performance in deep quantized neural network inference, training remains a task primarily executed using floating point arithmetic. This is because both high dynamic range and numerical accuracy are central to the success of most modern training algorithms… However, due to its potential for computational, storage and energy advantages in hardware accelerators, neural network training methods that can be implemented with low precision integer-only arithmetic remains an active research challenge. In this […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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