Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learners are combined to obtain a joint decision. Recently, various methods have been explored in literature for estimating decision uncertainties using ensemble learning; however, determining which metrics are a better fit for certain decision-making applications remains a challenging task. In this paper, we study the following key research question in the selection of uncertainty metrics: when does an uncertainty metric outperforms another? We answer this question via a rigorous analysis of two commonly used uncertainty metrics in ensemble learning, namely ensemble mean and ensemble variance. We show that, under mild assumptions on the ensemble learners, ensemble mean is preferable with respect to ensemble variance as an uncertainty metric for decision making.

To efficiently run DNNs on the edge/cloud, many new DNN inference accelerators are being designed and deployed frequently. To enhance the resource efficiency of DNNs, model quantization is a widely-used approach. However, different accelerator/HW has different resources leading to the need for specialized quantization strategy of each HW. Moreover, using the same quantization for every layer may be sub-optimal, increasing the designspace of possible quantization choices. This makes manual-tuning infeasible. Recent work in automatically determining quantization for each layer is driven by optimization methods such as reinforcement learning. However, these approaches need re-training the RL for every new HW platform. We propose a new way for autonomous quantization and HW-aware tuning. We propose a generative model, AQGAN, which takes a target accuracy as the condition and generates a suite of quantization configurations. With the conditional generative model, the user can autonomously generate different configurations with different targets in inference time. Moreover, we propose a simplified HW-tuning flow, which uses the generative model to generate proposals and execute simple selection based on the HW resource budget, whose process is fast and interactive. We evaluate our model on five of the widely-used efficient models on the ImageNet dataset. We compare with existing uniform quantization and state-of-the-art autonomous quantization methods. Our generative model shows competitive achieved accuracy, however, with around two degrees less search cost for each design point. Our generative model shows the generated quantization configuration can lead to less than 3.5% error across all experiments.

In this free issue: current machine learning deep learning trends, news, resources, sneak preview of paid subscriber content. Having a searchable blog that requires authentication allows us to show every one what kind of resources are available. Free signups get previews and paid subscribers can quickly access and search for relevant resources. We also link to our Medium blog networks this way we have all the information in one place, organized by topics and keywords. Current easter eggs We routinely send easter eggs to paid subscribers.

Transformers and pre-trained models can be considered one of the most important developments in the recent years of deep learning. Beyond the research breakthroughts, Transformers have redefined the natural language understanding(NLU) space sparking a race between lead AI vendors to build bigger and more efficient neural networks. The Transformer architecture has been behind famous models such as Google's BERT, Facebook's RoBERTa or OpenAI's GPT-3. Is not surprising that many people believe that only big companies have the resources to tackle the implementation of Transformer models. Earlier this year, the deep learning community was astonished when Microsoft Research unveiled the Turing Natural Language Generation (T-NLG) model which, at the time, was considered the largest natural language processing(NLP) model in the history of artificial intelligence(AI) with 17 billion parameters.

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Training with artificial images is becoming increasingly important to address the lack of real data sets in various niche areas. Yet, many today's approaches write 2D/3D simulations from scratch. To improve this situation and make better use of existing pipelines, we've been working towards an integration between Blender, an open-source real-time physics enabled animation software, and PyTorch. Today we announce blendtorch, an open-source Python library that seamlessly integrates distributed Blender renderings into PyTorch data pipelines at 60FPS (640x480 RGBA). Batch visualization from 4 Blender instances running a physics enabled falling cubes scene.

Whether you've noticed it or not, Deep Learning (DL) plays an important part in all our lives. From the voice assistants and auto-correct services on your smartphone to the automation of large industries, deep learning is the underlying concept behind these meteoric rises in human progress. A major concept that we implement in deep learning is that of neural networks. A neural network is a computing algorithm of an interconnected system of mathematical formulae used to make predictions by "training" the algorithm on data relevant to the prediction to be made. This is partly inspired by the way neurons are connected in biological brains.

Deep Learning based Network Detection and Response technology leader included in "America's Most Promising Artificial Intelligence Companies"Blue Hexagon, deep learning innovator of Cyber AI You Can Trust was recognized in the 2020 Forbes AI 50 list. As one of America's most promising artificial intelligence (AI) companies, Blue Hexagon is the only real time deep learning cybersecurity company to instantly stop zero-day malware and threats before infiltration, detect and block active adversaries and reduce SOC alert overload."Traditional We are able to achieve 99.8% threat detection accuracy and sub-second verdict speed with our deep learning technology to revolutionize security operations," said Nayeem Islam, CEO of Blue Hexagon. "Forbes included us for using artificial intelligence in meaningful business-oriented ways. We're proud to be included in their list, and believe AI will fundamentally change the way we protect against cyber threats."In

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This lossless compressor achieves a much higher compression rate on English texts than general purpose compressors. Its typical compression ratio is 15% (number of output bits divided by the number of input bits). The compression is achieved by using the probability of the next word computed by the GPT-2 language model released by OpenAI. It is a neural network of 345 million parameters based on the Transformer architecture (the largest GPT-2 model of 1.5 billion parameters brings marginal improvement when compressing short messages). An arithmetic coder generates the bit stream.