Презентации с ML/DL секции Russian Supercomputing Days:
технологические аспекты от Mikhail Burtsev и Dmitry Korobchenko + обзор инвестиционной среды от Russia.AI
http://www.russia.ai/single-post/2016/10/10/Deep-Learning-%E2%80%93-Present-and-Future-of-AI-Slides-from-Russian-Supercomputing-Days-Conference
технологические аспекты от Mikhail Burtsev и Dmitry Korobchenko + обзор инвестиционной среды от Russia.AI
http://www.russia.ai/single-post/2016/10/10/Deep-Learning-%E2%80%93-Present-and-Future-of-AI-Slides-from-Russian-Supercomputing-Days-Conference
Обзор курсов по Deep Learning
Последнее время все больше и больше достижений в области искусственного интеллекта связано с инструментами глубокого обучения или deep learning. Мы решили разобраться, где же можно научиться необходимым навыкам, чтобы стать специалистом в этой области.
Последнее время все больше и больше достижений в области искусственного интеллекта связано с инструментами глубокого обучения или deep learning. Мы решили разобраться, где же можно научиться необходимым навыкам, чтобы стать специалистом в этой области.
Clockwork Convnets for Video Semantic Segmentation.
Adaptive video processing by incorporating data-driven clocks.
We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video.
https://arxiv.org/pdf/1608.03609v1.pdf
https://github.com/shelhamer/clockwork-fcn
http://www.gitxiv.com/posts/89zR7ATtd729JEJAg/clockwork-convnets-for-video-semantic-segmentation
#Caffe #video #Segmentation
Adaptive video processing by incorporating data-driven clocks.
We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video.
https://arxiv.org/pdf/1608.03609v1.pdf
https://github.com/shelhamer/clockwork-fcn
http://www.gitxiv.com/posts/89zR7ATtd729JEJAg/clockwork-convnets-for-video-semantic-segmentation
#Caffe #video #Segmentation
Multifaceted Feature Visualization.
Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks.
We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization.
https://github.com/Evolving-AI-Lab/mfv
https://arxiv.org/pdf/1602.03616v2.pdf
http://www.gitxiv.com/posts/Kqy2rHju5EsqpC32N/multifaceted-feature-visualization
Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks.
We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization.
https://github.com/Evolving-AI-Lab/mfv
https://arxiv.org/pdf/1602.03616v2.pdf
http://www.gitxiv.com/posts/Kqy2rHju5EsqpC32N/multifaceted-feature-visualization
Запись и презентация с вводного вебинара по DeepLearning от Александра Гончар для студентов СФ БашГУ.
https://www.youtube.com/watch?v=8pbQ9Pve8bo
https://www.linkedin.com/in/alex-honchar-4423b962
https://www.youtube.com/watch?v=8pbQ9Pve8bo
https://www.linkedin.com/in/alex-honchar-4423b962
Когда мы сможем загрузить мозг в компьютер
Специалист по машинному обучению, создатель одной из сильнейших шахматных программ Сергей Марков прочитал в Москве лекцию о перспективах перемещения сознания на другой носитель. О прогрессе в области нейроинтерфейсов, первых киборгах и перспективах нейронных сетей — в материале «Футуриста».
http://futurist.ru/articles/558
Специалист по машинному обучению, создатель одной из сильнейших шахматных программ Сергей Марков прочитал в Москве лекцию о перспективах перемещения сознания на другой носитель. О прогрессе в области нейроинтерфейсов, первых киборгах и перспективах нейронных сетей — в материале «Футуриста».
http://futurist.ru/articles/558
LipNet: Automated lipreading - Чтение по губам.
93% распознавание сетью (люди 52%)
Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction.
http://www.oxml.co.uk/publications/2016-Assael_Shillingford_LipNet.pdf
https://www.youtube.com/watch?v=fa5QGremQf8
93% распознавание сетью (люди 52%)
Lipreading is the task of decoding text from the movement of a speaker’s mouth. Traditional approaches separated the problem into two stages: designing or learning visual features, and prediction.
http://www.oxml.co.uk/publications/2016-Assael_Shillingford_LipNet.pdf
https://www.youtube.com/watch?v=fa5QGremQf8