When Steve Jobs demoed his NeXT computer at a 1988 user meeting, Charles Mann was there to record it—along with dozens of other talks by computing pioneers. Read More
Daily Archives: September 17, 2020
How to Select the Right Machine Learning Algorithm
Seven key factors to consider when implementing an algorithm
or any given machine learning problem, numerous algorithms can be applied and multiple models can be generated. … Having a wealth of options is good, but deciding on which model to implement in production is crucial. …Here is the list of factors to consider when implementing an algorithm:
- Interpretability
- The number of data points and features
- Data format
- Linearity of data
- Training time
- Prediction time
- Memory requirements
#machine-learning
Uncovering the structure of clinical EEG signals with self-supervised learning
Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data,such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based approaches. However, in most situations, unlabeled data is available in abundance. By extracting information from this unlabeled data, it might be possible to reach competitive performance with deep neural networks despite limited access to labels.Approach.We investigated self-supervised learning (SSL), a promising technique for discovering structure in unlabeled data, to learn representations of EEG signals. Specifically,we explored two tasks based on temporal context prediction as well as contrastive predictive coding on two clinically-relevant problems: EEG-based sleep staging and pathology detection.We conducted experiments on two large public datasets with thousands of recordings and performed baseline comparisons with purely supervised and hand-engineered approaches.Main results.Linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available. Additionally, the embeddings learned with each method revealed clear latent structures related to physiological and clinical phenomena, such as age effects.Significance.We demonstrate the benefit of self-supervised learning approaches on EEG data. Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data. Read More
Does BERT Solve Commonsense Task via Commonsense Knowledge?
The success of pretrained contextualized language models such as BERT motivates a line of work that investigates linguistic knowledge inside such models in order to explain the huge improvement in downstream tasks.While previous work shows syntactic, semantic and word sense knowledge in BERT, little work has been done on investigating how BERT solves Commonsense QA tasks. In particular,it is an interesting research question whether BERT relies on shallow syntactic patterns or deeper commonsense knowledge for disambiguation. We propose two attention-based methods to analyze commonsense knowledge inside BERT, and the contribution of such knowledge for the model prediction. We find that attention heads successfully capture the structured commonsense knowledge encoded in CONCEPTNET, which helps BERT solve commonsense tasks directly. Fine-tuning further makes BERT learn to use the common-sense knowledge on higher layers. Read More
DeepSpeed: Extreme-scale model training for everyone
In February, we announced DeepSpeed, an open-source deep learning training optimization library, and ZeRO (Zero Redundancy Optimizer), a novel memory optimization technology in the library, which vastly advances large model training by improving scale, speed, cost, and usability.
… Today, we are happy to share our new advancements that not only push deep learning training to the extreme, but also democratize it for more people—from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. More specifically, DeepSpeed adds four new system technologies that further the AI at Scale initiative to innovate across Microsoft’s AI products and platforms. These offer extreme compute, memory, and communication efficiency, and they power model training with billions to trillions of parameters. Read More
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