Computer software can now estimate the calorie content of food simply from a photo of a meal. Calorie counting is one of the ways many people try to control their weight, but manually inserting nutritional information about products into apps is time consuming. Cooking meals muddles things further, making it difficult to ascertain accurate calorie counts. Robin Ruede and his colleagues at the Karlsruhe Institute of Technology, Germany, might be able to help.

We are accelerating fast into an Artificial Intelligence (AI) driven digital era. Not a moment goes when digital is not part of our daily lives. And that's not just about smart devices at home or collaborating on MS Teams or Zoom meetings but extends to cars we drive, payments we make or shopping we do. While so much of our lives are surrounded and enhanced by digital experiences, when it comes to the most crucial resource that helps companies achieve goals and scale to new heights, that is human resources, AI is a tiny component. It will be a pity if we can't extend and use the very tools that make our lives so much better when it comes to talent or human resources management.

PyCaret is a Python open source machine learning library designed to make performing standard tasks in a machine learning project easy. It is a Python version of the Caret machine learning package in R, popular because it allows models to be evaluated, compared, and tuned on a given dataset with just a few lines of code. The PyCaret library provides these features, allowing the machine learning practitioner in Python to spot check a suite of standard machine learning algorithms on a classification or regression dataset with a single function call. In this tutorial, you will discover the PyCaret Python open source library for machine learning. A Gentle Introduction to PyCaret for Machine Learning Photo by Thomas, some rights reserved.

We are interested in learning models of non-stationary environments, which can be framed as a multi-task learning problem. Model-free reinforcement learning algorithms can achieve good asymptotic performance in multi-task learning at a cost of extensive sampling, due to their approach, which requires learning from scratch. While model-based approaches are among the most data efficient learning algorithms, they still struggle with complex tasks and model uncertainties. Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task. In this paper, we propose a meta-reinforcement learning approach to learn the dynamic model of a non-stationary environment to be used for meta-policy optimization later. Due to the sample efficiency of model-based learning methods, we are able to simultaneously train both the meta-model of the non-stationary environment and the meta-policy until dynamic model convergence. Then, the meta-learned dynamic model of the environment will generate simulated data for meta-policy optimization. Our experiment demonstrates that our proposed method can meta-learn the policy in a non-stationary environment with the data efficiency of model-based learning approaches while achieving the high asymptotic performance of model-free meta-reinforcement learning.

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) that best matches the distribution of observations. Non-Bayesian social learning theory provides a framework that solves this problem in an efficient manner by allowing the agents to sequentially communicate and update their beliefs for each hypothesis over the network. Most existing approaches assume that agents have access to exact statistical models for each hypothesis. However, in many practical applications, agents learn the likelihood models based on limited data, which induces uncertainty in the likelihood function parameters. In this work, we build upon the concept of uncertain models to incorporate the agents' uncertainty in the likelihoods by identifying a broad set of parametric distribution that allows the agents' beliefs to converge to the same result as a centralized approach. Furthermore, we empirically explore extensions to non-parametric models to provide a generalized framework of uncertain models in non-Bayesian social learning.

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models that "understand" language. In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model's inputs. Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs. We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations.

A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers for evaluating a group's mean performance, when qualitative grades instead of numerical scores are used for assessing its members' individual performance. Also, a new technique is developed for solving Linear Programming problems with fuzzy coefficients and everyday life applications are presented to illustrate our results.

We prove a new concentration inequality for U-statistics of order two for uniformly ergodic Markov chains. Working with bounded $\pi$-canonical kernels, we show that we can recover the convergence rate of Arcones and Gine (1993) who proved a concentration result for U-statistics of independent random variables and canonical kernels. Our proof relies on an inductive analysis where we use martingale techniques, uniform ergodicity, Nummelin splitting and Bernstein's type inequality where the spectral gap of the chain emerges. Our result allows us to conduct three applications. First, we establish a new exponential inequality for the estimation of spectra of trace class integral operators with MCMC methods. The novelty is that this result holds for kernels with positive and negative eigenvalues, which is new as far as we know. In addition, we investigate generalization performance of online algorithms working with pairwise loss functions and Markov chain samples. We provide an online-to-batch conversion result by showing how we can extract a low risk hypothesis from the sequence of hypotheses generated by any online learner. We finally give a non-asymptotic analysis of a goodness-of-fit test on the density of the invariant measure of a Markov chain. We identify the classes of alternatives over which our test based on the L2 distance has a prescribed power.

Imagine a classroom in the future where teachers are working alongside artificial intelligence partners to ensure no student gets left behind. The AI partner’s careful monitoring picks up on a student in the back who has been quiet and still for the whole class and the AI partner prompts the teacher to engage the student. When called on, the student asks a question. The teacher clarifies the material that has been presented and every student comes away with a better understanding of the lesson. This is part of a larger vision of future classrooms where human instruction and AI technology interact to improve educational environments and the learning experience. James Pustejovsky, the TJX Feldberg Professor of Computer Science, is working towards that vision with a team led by the University of Colorado Boulder, as part of the new $20 million National Science Foundation-funded AI Institute for Student-AI Teaming. The research will play a critical role in helping ensure the AI agent is a natural partner in the classroom, with language and vision capabilities, allowing it to not only hear what the teacher and each student is saying, but also notice gestures (pointing, shrugs, shaking a head), eye gaze, and facial expressions (student attitudes and emotions).

Machine learning PhD students are in a unique position: they often need to run large-scale experiments to conduct state-of-the-art research but they don't have the support of the platform teams that industrial ML engineers can rely on. As former PhD students ourselves, we recount our hands-on experience with these challenges and explain how open-source tools like Determined would have made grad school a lot less painful. When we started graduate school as PhD students at Carnegie Mellon University (CMU), we thought the challenge laid in having novel ideas, testing hypotheses, and presenting research. Instead, the most difficult part was building out the tooling and infrastructure needed to run deep learning experiments. While industry labs like Google Brain and FAIR have teams of engineers to provide this kind of support, independent researchers and graduate students are left to manage on their own.