Understanding the world around us via visual representations of it is one of the magical cognitive skills of the human brain. In some context, the brain can be considered this giant engine that constantly processes visual signals, extracts the relevant knowledge and triggers the corresponding actions. Although we don't quite yet understand how our brain forms fragments of knowledge from visual representations, the processes are embedded in our education methodologies. When we show a picture of a flower to a baby and tell him it's a rose, that's probably enough for the baby to start recognizing roses in the real world, whether they are in a vase or in a garden, one or many, red or white. At the same time is able to answer all sorts of questions related to roses.

I will describe two methods to visualize the conv. Let's check the layer names: I will select few conv. To get outputs from the selected layers, we will use Keras layer.output Then append the outputs on a list, Let's see: Next step is important, as we will instantiate a new model, which will take a random image (of either cat or dog) as input and the outputs will be the selected conv. Check the Keras Model API for more details.

Remedying this weakness is a key challenge in the quest for building intelligent agents that can learn continually when deployed in the real world, where their experiences are not necessarily i.i.d. and their resources may be limited. In my PhD, I have studied catastrophic forgetting in the context of deep reinforcement learning, where changes to the distribution of an agent's experiences arise from multiple sources and occur unpredictably over the course of learning. Inspired partially by the processes of synaptic consolidation and systems consolidation in the brain, I will present two methods that harness multi-timescale processes to mitigate catastrophic forgetting in an RL setting. Bio: Christos is currently pursuing a PhD on the topic of Continual Reinforcement Learning at Imperial College London, co-supervised by Claudia Clopath (Bioengineering) and Murray Shanahan (Computing). He graduated with a BA in Applied Mathematics from Harvard and worked as a trader at Brevan Howard for several years, before leaving to pursue MScs in Computing and Informatics at Imperial College and Edinburgh University respectively, driven by an interest in computational neuroscience and machine learning. In April, he will start a job as a Research Scientist at DeepMind.

This course on Artificial Intelligence for Imaging is a unique opportunity to join a community of leading edge practitioners in the field of Quantitative Medical Imaging. During this 4-days immersive course, you will be able to attend lectures and workshops from world-class experts in Radiomics, Deep Learning, Synthetic Data, and Distributed Learning. You can also bring your own curated dataset with you for the hackathon (labelled, sorted by outcome, open source or fully anonymised, and cleared by ethics). If requested ahead of time, we will perform "data matching" for attendees to facilitate external cross validation. There will be ample opportunity to network with faculty members, other participants and companies.

It's very abstract and it's something that deep neural networks can't do even in the simplest form. They can't recognize same… If you give them an example of something in which two things are the same versus two things are different in general, it can't do that task.

OpenAI, the non-profit founded by Elon Musk in 2015 – he's no longer part of it – has released the biggest and final version of the GPT-2 text-generating language model, which it has admitted could be dangerous in the wrong hands. However, it says the newly released full model's output is only slightly more convincing to humans than the previous version. The organization released the first portion of the model in February as part of a staged process, beginning with just 124 million parameters. It held back the full model with 1.5 billion parameters because scientists believed it was too dangerous and could be used by malicious actors, such as terrorists and state-sponsored hackers. Among the malicious purposes for which OpenAI admitted GPT-2 might be used are generating misleading news articles, impersonating others online, automating the production of abusive or fake content for social media, and automating the creation of spam and phishing content.

Language models (LMs) are the hottest topic in the NLP research right now. The most prominent examples are BERT and GPT-2 but new LMs are published every month trained on humongous volumes of text. Are LMs capable of encoding knowledge in a way similar to knowledge graphs? Petroni et al study this problem comparing language models with knowledge graphs on Question Answering and NLG tasks where factual knowledge is required, e.g., a question is posed by inserting a MASK token instead of an answer. Turns out LMs demonstrate similar to KGs performance on very simple questions such as "Adolphe Adam died in [Paris]" .

The impressive performance of neural networks on natural language processing tasks attributes to their ability to model complicated word and phrase interactions. Existing flat, word level explanations of predictions hardly unveil how neural networks handle compositional semantics to reach predictions. To tackle the challenge, we study hierarchical explanation of neural network predictions. We identify non-additivity and independent importance attributions within hierarchies as two desirable properties for highlighting word and phrase interactions. We show prior efforts on hierarchical explanations, e.g. contextual decomposition, however, do not satisfy the desired properties mathematically. In this paper, we propose a formal way to quantify the importance of each word or phrase for hierarchical explanations. Following the formulation, we propose Sampling and Contextual Decomposition (SCD) algorithm and Sampling and Occlusion (SOC) algorithm. Human and metrics evaluation on both LSTM models and BERT Transformer models on multiple datasets show that our algorithms outperform prior hierarchical explanation algorithms. Our algorithms apply to hierarchical visualization of compositional semantics, extraction of classification rules and improving human trust of models.

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.

This is indicated, among others, by several review and overview publications (Ching et al., 2018; Jiang et al., 2017; Litjens et al., 2017; Miotto, Wang, Wang, Jiang, & Dudley, 2017). In particular, the capabilities of end-to-end deep learning approaches on such supervised learning tasks are highly promising. For instance, vast advances have been reported in the literature regarding cancer diagnosis with deep neural networks (Hu et al., 2018). End-to-end deep learning refers to a trend involving deep (neural network) model architectures which are able to learn highly complex relationships between predictors and the target variable while having less parameters than traditional (more shallow) models with comparable performance (Goodfellow, Bengio, & Courville, 2016). In the training process, highly complex features are derived automatically by the learning algorithm (LeCun, Bengio, & Hinton, 2015). This framework contrasts the traditional pipeline of domain specific data preprocessing and handcrafted features in combination with simpler prediction models. Despite all the recent success of machine learning, there are still challenges regarding over-optimistic conclusions drawn from finite datasets which may to a large extend be attributed to the following two (broad) categories: 1. Study design and reporting: The most popular recommendation to split data for training, selection and evaluation is frequently employed in practice (Friedman, Hastie, & Tibshirani, 2009; Géron, 2017; Goodfellow et al., 2016; Japkowicz & Shah, 2011; Kuhn & Johnson, 2013; Zheng, 2015). In the ML community, the according datasets are commonly denoted as training, validation and test set.