Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were unseen in their training environments. To address this problem, we introduce a robust representation learning approach for RL. We introduce an auxiliary objective based on the multi-view information bottleneck (MIB) principle which encourages learning representations that are both predictive of the future and less sensitive to task-irrelevant distractions. This enables us to train high-performance policies that are robust to visual distractions and can generalize to unseen environments. We demonstrate that our approach can achieve SOTA performance on challenging visual control tasks, even when the background is replaced with natural videos. In addition, we show that our approach outperforms well-established baselines on generalization to unseen environments using the large-scale Procgen benchmark.

Modern machine learning algorithms excel when the training and test distributions match but often fail under even moderate distribution shift (Beery et al., 2018); learning a predictor which generalizes to distributions which differ from the training data is therefore an important task. This objective, broadly referred to as out-of-distribution (OOD) generalization, is not realizable in general, so researchers have formalized several possible restrictions. Common choices include a structural assumption such as covariate or label shift (Widmer & Kubat, 1996; Bickel et al., 2009; Lipton et al., 2018) or expecting that the test distribution will lie in some uncertainty set around the training distribution (Bagnell, 2005; Rahimian & Mehrotra, 2019). One popular assumption is that the training data is comprised of a collection of "environments" (Blanchard et al., 2011; Muandet et al., 2013; Peters et al., 2016) or "groups" (Sagawa et al., 2020), each representing a distinct distribution, where the group identity of each sample is known. The hope is that by cleverly training on such a combination of groups, one can derive a robust predictor which will better transfer to unseen test data which relates to the observed distributions--such a task is known as domain generalization.

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches.

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several learning-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box in its most basic form, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: ConCrete MAP Detection (CMD). Furthermore, by extending CMD to the idea of deep unfolding, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMD based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today's communication systems. Numerical simulation results in MIMO systems reveal CMD to feature a promising performance complexity trade-off compared to SotA. Notably, we demonstrate CMD's soft outputs to be reliable for decoders.

Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up.

When I was in graduate school in the 1990s, one of my favorite classes was neural networks. Back then, we didn't have access to TensorFlow, PyTorch, or Keras; we programmed neurons, neural networks, and learning algorithms by hand with the formulas from textbooks. We didn't have access to cloud computing, and we coded sequential experiments that often ran overnight. There weren't platforms like Alteryx, Dataiku, SageMaker, or SAS to enable a machine learning proof of concept or manage the end-to-end MLops lifecycles. I was most interested in reinforcement learning algorithms, and I recall writing hundreds of reward functions to stabilize an inverted pendulum.

Artificial intelligence (AI) and machine learning (ML) have moved from the backrooms of computer science into the mainstream. Their impact is being felt in everything from how we shop to money markets and medical research. Larger models have been trained in separated modality. For instance, GPT-3 is the first 100-billion-parameter model for natural language processing (NLP). Recently, a-trillion-parameter model (T5-XXL) has also been trained.

Master Python By Implementing Face Recognition & Image Processing In Python Created by Emenwa Global, Zoolord AcademyPreview this Course - GET COUPON CODE Computer vision is an interdisciplinary field that deals with how computers can be made to gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.

Medb Corcoran, Ireland lead for Accenture Labs, talks about her career journey and why data skills will be critical for businesses going forward. As the Ireland lead for Accenture Labs, Medb Corcoran oversees a team of AI researchers that seeks to address critical business problems with tools such as machine learning, natural language processing and knowledge representation. This team is based at The Dock, Accenture's R&D and innovation centre in Dublin. Corcoran is also the company's global responsible AI lead for technology innovation, helping organisations integrate a data-driven assessment of algorithmic fairness into their processes. Here, she reflects on her career and why she believes workplaces of the future will rely on data skills like never before.

We need to keep the essence of human-centric AI. This is at least how we see things in the EU. We don't want a form of AI that will replace everything - we want AI that will complement what we do, and to leave more space for us to be creative and have a better quality of life. One individual who is already seeing that creativity first-hand is Frank Salzgeber, head of innovation at the European Space Agency's (ESA) venture office. His organisation supports 220 new start-ups a year.