In this work, we consider the setting of learning problems under a wide class of spectral risk (or "L-risk") functions, where a Lipschitz-continuous spectral density is used to flexibly assign weight to extreme loss values. We obtain excess risk guarantees for a derivative-free learning procedure under unbounded heavy-tailed loss distributions, and propose a computationally efficient implementation which empirically outperforms traditional risk minimizers in terms of balancing spectral risk and misclassification error.

We propose a new unified framework for causal inference when outcomes depend on how agents are linked in a social or economic network. Such network interference describes a large literature on treatment spillovers, social interactions, social learning, information diffusion, social capital formation, and more. Our approach works by first characterizing how an agent is linked in the network using the configuration of other agents and connections nearby as measured by path distance. The impact of a policy or treatment assignment is then learned by pooling outcome data across similarly configured agents. In the paper, we propose a new nonparametric modeling approach and consider two applications to causal inference. The first application is to testing policy irrelevance/no treatment effects. The second application is to estimating policy effects/treatment response. We conclude by evaluating the finite-sample properties of our estimation and inference procedures via simulation.

Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in the direction of research.

In this paper, the Robotic Assistant Agent for student and machine co-learning on AI-FML practice with AIoT application is presented. The structure of AI-FML contains three parts, including fuzzy logic, neural network, and evolutionary computation. Besides, the Robotic Assistant Agent (RAA) can assist students and machines in co-learning English and AI-FML practice based on the robot Kebbi Air and AIoT-FML learning tool. Since Sept. 2019, we have introduced an Intelligent Speaking English Assistant (ISEA) App and AI-FML platform to English and computer science learning classes at two elementary schools in Taiwan. We use the collected English-learning data to train a predictive regression model based on students' monthly examination scores. In Jan. 2021, we further combined the developed AI-FML platform with a novel AIoT-FML learning tool to enhance students' interests in learning English and AI-FML with basic hands-on practice. The proposed RAA is responsible for reasoning students' learning performance and showing the results on the AIoT-FML learning tool after communicating with the AI-FML platform. The experimental results and the collection of students' feedback show that this kind of learning model is popular with elementary-school and high-school students, and the learning performance of elementary-school students is improved.

Redeem Get Udemy Coupon What you'll learn Udemy Coupon Best Description Search Algorithms and Optimization techniques are the engines of most Artificial Intelligence techniques and Data Science. There is no doubt that Hill Climbing and Simulated Annealing are the most well-regarded and widely used AI search techniques. A lot of scientists and practitioners use search and optimization algorithms without understanding their internal structure. However, understanding the internal structure and mechanism of such AI problem-solving techniques will allow them to solve problems more efficiently. This also allows them to tune, tweak, and even design new algorithms for different projects.

Do you want to make your career in Data Science? Want to have a successful career and a life worth inspiring? All you need is the will to succeed and the passion to learn!!! Python being one of the most widely used languages is the new mantra for success. It is the number one tool for analytical professionals and is one of the top programming languages in the year 2019. Our aim is to make the students get acquainted with python and become proficient in the most popular programming language.

As part of the MIT Task Force on the Work of the Future's series of research briefs, Professor Thomas Malone, Professor Daniela Rus, and Robert Laubacher collaborated on "Artificial Intelligence and the Future of Work," a brief that provides a comprehensive overview of AI today and what lies at the AI frontier. The authors delve into the question of how work will change with AI and provide policy prescriptions that speak to different parts of society. Thomas Malone is director of the MIT Center for Collective Intelligence and the Patrick J. McGovern Professor of Management in the MIT Sloan School of Management. Daniela Rus is director of the Computer Science and Artificial Intelligence Laboratory, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, and a member of the MIT Task Force on the Work of the Future. Robert Laubacher is associate director of the MIT Center for Collective Intelligence.

In this blog, we shall discuss about how to build a neural network to translate from English to German. This problem appeared as the Capstone project for the coursera course "Tensorflow 2: Customising your model", a part of the specialization "Tensorflow2 for Deep Learning", by the Imperial College, London. The problem statement / description / steps are taken from the course itself. We shall use the concepts from the course, including building more flexible model architectures, freezing layers, data processing pipeline and sequence modelling. Here we shall use a language dataset from http://www.manythings.org/anki/

In the field of machine learning, the well-trained model is assumed to be able to recover the training labels, i.e. the synthetic labels predicted by the model should be as close to the ground-truth labels as possible. Inspired by this, we propose a self-guided curriculum strategy to encourage the learning of neural machine translation (NMT) models to follow the above recovery criterion, where we cast the recovery degree of each training example as its learning difficulty. Specifically, we adopt the sentence level BLEU score as the proxy of recovery degree. Different from existing curricula relying on linguistic prior knowledge or third-party language models, our chosen learning difficulty is more suitable to measure the degree of knowledge mastery of the NMT models. Experiments on translation benchmarks, including WMT14 English$\Rightarrow$German and WMT17 Chinese$\Rightarrow$English, demonstrate that our approach can consistently improve translation performance against strong baseline Transformer.

Geometry problem solving has attracted much attention in the NLP community recently. The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge. However, current datasets are either small in scale or not publicly available. Thus, we construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language. We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS). Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively. Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step. A theorem predictor is also designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path. Extensive experiments on the Geometry3K and GEOS datasets demonstrate Inter-GPS achieves significant improvements over existing methods.