Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples. However, conventional time-series classification algorithms fail to tackle the few-shot scenario. Existing few-shot learning methods are proposed to tackle image or text data, and most of them are neural-based models that lack interpretability. This paper proposes an interpretable neural-based framework, namely \textit{Dual Prototypical Shapelet Networks (DPSN)} for few-shot time-series classification, which not only trains a neural network-based model but also interprets the model from dual granularity: 1) global overview using representative time series samples, and 2) local highlights using discriminative shapelets. In particular, the generated dual prototypical shapelets consist of representative samples that can mostly demonstrate the overall shapes of all samples in the class and discriminative partial-length shapelets that can be used to distinguish different classes. We have derived 18 few-shot TSC datasets from public benchmark datasets and evaluated the proposed method by comparing with baselines. The DPSN framework outperforms state-of-the-art time-series classification methods, especially when training with limited amounts of data. Several case studies have been given to demonstrate the interpret ability of our model.

The importance of solvation or hydration mechanism and accompanying free energy change has made various in silico calculation methods for the solvation energy one of the most important application in computational chemistry[1-25]. The solvation free energy directly influences many chemical properties in solvated phases and plays a dominant role in various chemical reactions: drug delivery[2, 16, 18, 26], organic synthesis[27], electrochemical redox reactions[28-31], etc. The atomistic computer simulation approaches for the solvent and the solute molecules directly offer the microscopic structure of the solvation shell, which surrounds the solutes molecule[7, 8, 13, 17, 18, 32]. The solvation shell structure could provide us detailed physicochemical information like microscopic mechanisms on solvation or the interplay between the solvent and the solute molecules when we use an appropriate force field and molecular dynamics parameters. However, those explicit solvation methods we stated above need an extensive amount of numerical calculations since we have to simulate each individual molecule in the solvated system. The practical problems on the explicit solvation model restrict its applications to classical molecular mechanics simulations[7, 8, 17] or a limited number of QM/MM approaches[13, 32]. For classical mechanics approaches for macromolecules or calculations for small compounds at quantum-mechanical level, the idea of implicit solvation enables us to calculate solvation energy with feasible time and computational costs when one considers a given solvent as a continuous and isotropic medium in the Poisson-Boltzmann equation[1, 3-6, 9, 11, 15, 23, 24]. Many theoretical advances have been introduced to construct the continuum solvation model, which involves parameterized solvent properties: the polarizable continuum model (PCM)[9], the conductor-like screening model (COSMO)[1] and its variations[6, 33], generalized Born approximations like solvation model based on density (SMD)[5] or solvation model 6, 8, 12, etc. (SMx)[4, 11]. The structure-property relationship (SPR) is rather a new approach, which predicts the solvation free energy with a completely different point of view when compared to computer simulation approaches with precisely defined theoretical backgrounds[34, 35].

Human-centered collaborative systems require proactive robot behavior with precise timing, which in turn mandates awareness of human actions, state of the environment and the task being executed, [1-4]. Proactive robot behavior is achieved by (1) recognizing the current state of the human collaborator and the environment based on real-time observations, (2) human action prediction given the observations and the model of the task, and (3) generating robot actions in line with the prediction. Human action recognition may however be highly uncertain if the human collaborator is not executing a strictly defined task plan. This is true regardless of whether perception is based on motion-capture devices or image based pose estimation. For a robot to act in a proactive manner, while at the same time avoiding actions when the risk of making a mistake is too high, it is essential for the action-decision system to take this uncertainty into consideration. We therefore propose to train the perception system and the robot policy in an end-to-end fashion using reinforcement learning (RL). This is different from earlier studies in which human action recognition and prediction are typically decoupled from robot action policy training [3-7]. Our main objective is to improve the fluency in coordination between the human and robot partners by allowing the policy to explicitly weigh the benefits of timely actions to the risk of making a mistake when uncertainties are too high.

Recurrent Neural Networks (RNN) are a class of Artificial Neural Networks that can process a sequence of inputs in deep learning and retain its state while processing the next sequence of inputs. Traditional neural networks will process an input and move onto the next one disregarding its sequence. Data such as time series have a sequential order that needs to be followed in order to understand. Traditional feed-forward networks cannot comprehend this as each input is assumed to be independent of each other whereas in a time series setting each input is dependent on the previous input. In Illustration 1 we see that the neural network (hidden state) A takes an xt and outputs a value ht.

Click here to learn more about Gilad David Maayan. There are a significant number of investments in the automotive industry nowadays. The majority of these investments focus on artificial intelligence (AI) and the optimization of self-driving technology. Meanwhile, new mobility systems and players are making their way into the automotive market. Tesla is trying to improve its autopilot system, Uber is testing robo-taxis, and Google is developing self-driving cars.

Jeff Clune is the former Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming, a Senior Research Manager and founding member of Uber AI Labs, and currently a Research Team Leader at OpenAI. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff's research can be found at JeffClune.com

While BERT is a significant improvement in how computers'understand' human language, it is still far away from understanding language and context in the same way that humans do. We should, however, expect that BERT will have a significant impact on many understanding focused NLP initiatives. The General Language Understanding Evaluation benchmark (GLUE) is a collection of datasets used for training, evaluating, and analyzing NLP models relative to one another. The datasets are designed to test a model's language understanding and are useful for evaluating models like BERT. As the GLUE results show, BERT makes it possible to outperform humans even in comprehension tasks previously thought to be impossible for computers to outperform humans.

Step 3: Discover how to work through problems using machine learning in Python. Step 3: Discover how to work through problems using machine learning in Python.

Amazon recently bought up a self-driving autonomous ride-hailing startup Zoox, which is being claimed as the most ambitious step that the tech giant has taken in the recent past. Reportedly a $1.2 billion deal, the acquisition of the Robo-taxi company is not just to build upon its capabilities to deliver packages but actively set foot in the autonomous driving industry. While Amazon has invested heavily in developing drones or autonomous delivery robots in the past, its investment in self-driving vehicles has recently gained traction. Some of the other ventures of the company have been in self-driving truck Embark when CNBC reported that it had been hauling Amazon cargo on some of its test runs. For instance, in drones, Amazon has designed a future delivery system to safely deliver packages to customers in a short period of time.

The recent development of Artificial Intelligence has transformed the way multiple industries integrate and evaluate data, and implement the resulting insights to improve decision making. Applications of AI have infiltrated in everyday scenarios, making it the most demanding technology. Every business these days is pouring huge capital to harness the power of this tech and stay ahead of innovation. This is why a career in artificial intelligence has grown exponentially as companies need the right and qualified talent to meet their demands. Here we have accumulated top AI salaries, with highly-demanded job positions and biggest paydays in India in July 2020. An AI engineer is someone who has the ability to build AI models using machine learning algorithms and deep learning neural networks to draw business insights.