In this research, I look at how deep learning has been used in voice generation.
https://heartbeat.fritz.ai/a-2019-guide-to-speech-synthesis-with-deep-learning-630afcafb9dd
submitted by /u/mwitiderrick
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As now it is widespread that backpropagation is not a biologically plausible approach, I would like to raise a discussion around alternatives for the method.
In my mind, a cool idea would be to evaluate the outputs of each layer individually, i.e., what should we expect to see as output for the hidden layer number L? This would remove the need of backward sweeps (because a layer’s ‘accuracy’ would depend only of itself) and make transfer learning a lot easier (cause if it’s a layer-by-layer learning, we can put pieces together for similar task, with minor adjusments if necessary, e.g. the first layer of a CNN that identifies cats might be useful to identifying other felines).
However, nothing comes to my mind as to how we could achieve that. Because, as I see, this would require us to have labels (or at least some representations for us to compare what we’re getting to what we want) and I don’t think labels are required when we humans learn (at least not too many labels).
Anyway, I’d love to hear ideas from other minds, as I think this is the best way for us to come up with newer ideas.
Cheers guys, have a good one 🙂
submitted by /u/Berdas_
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Hello everyone! Working under one interesting problem, as you can read from post name, and wonder does anyone have ideas or hints for it? As we know autoencoders take input (in my case it’s an image from the popular dataset) and reconstruct it as an output. Let’s call input – node 1, output – node 3. It creates valuable features at its hidden layers (let’s call it node 2) during the process. Let’s hypothesize, that if node 2 is used as input for CNN then the classification will be improved. My current ideas are:
1 – For now, it sounds interesting and reasonable to try use output of the encoder – latent space representation as an input for following CNN.
2 – Use one of the decoder layers as input for CNN.
A possible purpose of it – try to get more important futures from class imbalanced data. (As an example – from 5 classes 1 of them contain 50% fewer images than other). Let’s discuss?
submitted by /u/brhrrr
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Forty percent of nursing home residents fall at least once a year, with one in five of these spills resulting in fractures or hospital stays.
Falling isn’t the only risk for eldercare residents. Those who are non- or partially mobile often suffer pressure ulcers, commonly known as bed sores, from not moving enough in their beds.
The risk of falls and ulcers dramatically increases in nursing homes that are short staffed. With staff stretched thin, their attention is divided across many rooms, many beds and many residents.
TeiaCare, based in Milan, Italy, wants to give caregivers a helping hand and ensure that nursing home residents get the attention they need, when they need it. The company offers the first digital assistant for long-term care that uses intelligent video analytics to make sure carers are alerted when their help is needed the most.
An aging population brings increasing demand for resources such as access to nursing homes. And the population of our planet is aging dramatically. In 2017, the UN estimates there were nearly a billion people aged 60 or over — about 13 percent of the global population, a proportion that is expected to soar.
However, the number of needed caregivers isn’t keeping up. By 2020, when nearly 20 percent of Europe’s population will be over 65, an estimated 800,000 more caregivers will be needed.
To reduce the risk of falls and bed sores, TeiaCare’s digital assistant, which consists of an optical sensor connected to a processor, is mounted onto ceilings. The processor uses a series of computer vision and deep learning algorithms, accelerated by NVIDIA GPUs, to analyze the visual data captured.
None of the video data is saved or stored. Instead, the system identifies specific movements and resting positions in real time.
The system then sends alerts directly to carers when attention needs to be given to a patient — perhaps they have fallen out of bed or have spent too long on one side and are at risk of developing an ulcer.
Each bed is tagged according to the patient’s individual requirements.
As well as real-time alerts, the system generates customized reports, giving staff an insight into patient movements, how long they’re spending in and out of bed, and allowing them to identify any areas for improving patient care.
Caregivers also see improvements to their working conditions — they know that they’ll be alerted if something happens to a resident and can take appropriate action. This means less stress for them and improved working efficiency.
For the facility owners, introducing the digital assistant means fewer liabilities, better quality of service and improved efficiency metrics.
And families enjoy better peace of mind by knowing that any falls will be immediately identified and their relatives will get the help they need.
TeiaCare is now expanding its assistant to monitor other behavioral and physiological traits.
Activity tracking will help improve the care of residents with dementia or Alzheimer’s disease as they tend to suffer from wandering. By monitoring patient movements, staff can ensure that their safety is not put at risk.
The company is also developing algorithms to monitor patients’ heart and breathing rates, using the same optical sensor-based system. This non-invasive way of monitoring resident vital signs could help ensure their comfort, prevent health deterioration and give families peace of mind.
TeiaCare is a member of the NVIDIA Inception virtual accelerator, which provides marketing and technology support to AI startups.
Image credit: sabinevanerp
The post How AI Is Helping Care for an Aging Population appeared first on The Official NVIDIA Blog.
Going through the Transformer paper, and its implementation, I have had a question:
In the self-attention routine in the encoder, is it plausible to compute q dot q instead of q dot k when calculating scores for each input token?
I see that in the self-attention, the memory_antecedent = query_antecedent and q, k, v is computed (and trained) separately (c.f. compute_qkv in T2T).
Would utilizing the same q for the computation of scores (rather than having a separate k) seriously deteriorate the performance?
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Hi all!
I was working on creating embeddings for medical concepts. These terms/phrases are used for annotating biomedical documents. Now usually the method of creating a co-occurrence matrix and then factorizing it to obtain dense, lower-dimensional vectors is termed as unsupervised learning since annotated data is not involved. I am using the same process but for the annotations themselves. Does this qualify as supervised learning since I need annotated data or does this qualify as unsupervised learning since the method of obtaining the embeddings is unsupervised?
submitted by /u/atif_hassan
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![[R]The Path to Nash Equilibrium](https://a.thumbs.redditmedia.com/c9TZv9D38uPJrXDuGbqA6DZS4WHt5sGBf_QSpVLyPJ8.jpg "[R]The Path to Nash Equilibrium") |
Main point: Nash equilibrium can be achieved without any beyond-player mediation, and the path towards it can be clearly visualized. https://arxiv.org/abs/1908.09021 Demos at Github to try. Fun guaranteed. And the following figures from paper shows visualizations of the paths towards Nash equilibrium: 3X3 two-person game. The triangle represents the probability simplex in 3-D space. 60X40 two-person game with 60 or 40 dimensions being reduced to 3 dimensions by PCA. Equilibrium point is always the final destination of strategy path. submitted by /u/lansiz |
I’m using a MLP to recognise gestures for my VR game.
The gesture gets preprocessed into a 2D grid, every grid cell becomes an input.
However I would like to add the direction into the recognition. My first thought was for each cell also input a 0 value for never entered and 0.5 – 1 for first – last cell.
Another idea was to add the start – end vector as two values. (x,y)
Any tips?
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Amazon SageMaker provides a fully-managed service for data science and machine learning workflows. One of the most important capabilities of Amazon SageMaker is its ability to run fully-managed training jobs to train machine learning models. Visit the service console to train machine learning models yourself on Amazon SageMaker.
Now, you can speed up your training job runs by training machine learning models from data stored in Amazon Elastic File System (EFS) or Amazon FSx for Lustre. Amazon EFS provides a simple, scalable, elastic file system for Linux-based workloads for use with AWS Cloud services and on-premises resources. Amazon FSx for Lustre is a high-performance file system optimized for workloads, such as machine learning, analytics, and high performance computing.
Training machine learning models requires providing the training datasets to the training job. When using Amazon Simple Storage Service (S3) as the training datasource in file input mode, all training data is downloaded from Amazon S3 to the EBS volumes attached to the training instances at the start of the training job. A distributed file system such as Amazon EFS or FSx for Lustre can speed up machine learning training by eliminating the need for this download step.
In this blog post, we go over the benefits of training your models using a file system, provide information to help you choose a file system, and show you how to get started.
When considering whether you should train your machine learning models from a file system the first thing to consider is: where does your training data reside now?
If your training data is already in Amazon S3 and your needs do not dictate a faster training time for your training jobs, you can get started with Amazon SageMaker with no need for data movement. However, if you need faster startup and training times we recommend that you take advantage of Amazon SageMaker’s integration with Amazon FSx for Lustre file system, which can speed up your training jobs by serving as a high-speed cache.
The first time you run a training job, if Amazon FSx for Lustre is linked to Amazon S3, it automatically loads data from Amazon S3 and makes it available to Amazon SageMaker at hundreds of gigabytes per second and submillisecond latencies. Additionally, subsequent iterations of your training job will have instant access to the data in Amazon FSx. Because of this, Amazon FSx has the most benefit to training jobs that have several iterations requiring multiple downloads from Amazon S3, or in workflows where training jobs must be run several times using different training algorithms or parameters to see which gives the best result.
If your training data is already in an Amazon EFS file system, we recommend choosing Amazon EFS as the file system data source. This choice has the benefit of directly launching your training jobs from the data in Amazon EFS with no data movement required, resulting in faster training start times. This is often the case in environments where data scientists have home directories in Amazon EFS, and are quickly iterating on their models by bringing in new data, sharing data with colleagues, and experimenting with which fields or labels to include. For example, a data scientist can use a Jupyter notebook to do initial cleansing on a training set, launch a training job from Amazon SageMaker, then use their notebook to drop a column and re-launch the training job, comparing the resulting models to see which works better.
After your training job completes, you can view the status history of the training job to observe the faster download time when using a file system data source.
With the addition of Amazon EFS and Amazon FSx for Lustre as data sources for training machine learning models in Amazon SageMaker, you now have greater flexibility to choose a data source that is suited to your use case. In this blog post, we used a file system data source to train machine learning models, resulting in faster training start times by eliminating the data download step.
Go here to start training machine learning models yourself on Amazon SageMaker or refer to our sample notebook to train a liner learner model using a file system data source to learn more.
Vidhi Kastuar is a Sr. Product Manager for Amazon SageMaker, focusing on making machine learning and artificial intelligence simple, easy to use and scalable for all users and businesses. Prior to AWS, Vidhi was Director of Product Management at Veritas Technologies. For fun outside work, Vidhi loves to sketch and paint, work as a career coach, and spend time with his family and friends.
Will Ochandarena is a Principal Product Manager on the Amazon Elastic File System team, focusing on helping customers use EFS to modernize their application architectures. Prior to AWS, Will was Senior Director of Product Management at MapR.
