Deep Learning Pioneers Yann LeCun and Yoshua Bengio Elected as AAAI-20 Fellows

December 28, 2019 sherry 2 Data Science, Design, Engineering, Technology,

Tags: A/B testing, AAAI, AAAI 2020, AAAI-20, AAAI-20 Fellows, Abstract, academia, acceleration, Accuracy, ACL, action, activation function, active learning, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI developments, AI methods, AI system, Algorithms, analytics, analytics space, and not the other way around, application design, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, Artificial Intelligence Journal, artificial intelligence-based systems, association, Association for the Advancement of Artificial Intelligence, attention, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, available, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, behavior, Bellman equation, bias (ethics/fairness), bias (math), biases, Big Data Analytics, bigram, binary classification, binning, boosting, broad spectrum, broadcasting, bucketing, calibration layer, Canada Research Chair in Statistical Learning Algorithms, candidate generation, candidate sampling, categorical data, centroid, centroid-based clustering, checkpoint, class, class-imbalanced dataset, classification, classification model, classification threshold, clipping, Cloud TPU, clustering, co-adaptation, collaborative filtering, comments, Community, Computational neural networks, computer vision, Conference, confirmation bias, confusion matrix, content, context, Continuous delivery, continuous feature, control, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, core principles, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, Cynthia Breazeal, data, data analysis, data augmentation, Data Quality, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), datasets, decision boundary, decision maker, decision making, decision threshold, decision tree, decision trees, decisions, deep learning, Deep Learning Pioneers, deep model, deep neural network, Deep Q-Network (DQN), delivering, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, designed, Developers, device, DevOps, Dialogue Bots, different hyperparameters, different initializations, different overall structure, dimension reduction, dimensions, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, domains, downsampling, DQN, Dragomir R. Radev, dropout regularization, dynamic model, eager execution, early stopping, economic and social obstacles, Education, effective removal, Elected, embedding space, embeddings, empirical risk minimization (ERM), engineering, ensemble, environment, equal groups, equal treatment, errors, especially, establish, ethical professional duty, Ethics of artificial intelligence, events, Evitar Matanya, Evitar Matanya Prof. Karine Nahon, excellent, experience, experimenter’s bias, Explaining of Israel, Fellows, fine tuning, Fintech, focus, forget gate, formal, framework, full softmax, fully autonomous, fully connected layer, Future of AI, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, Godfathers of AI, Google Dataset Search, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great, great success, greedy policy, ground truth, group attribution bias, hashing, Healthcare, helpful feedback, heuristic, hidden layer, hierarchical clustering, high-quality, highlight, hinge loss, holdout data, human, hyperparameter, hyperplane, i.i.d., ideas, image recognition, imbalanced dataset, impact, Implementation of principles, implicit bias, improvement, in every context, in-group bias, including, incompatibility of fairness metrics, increasing, increasingly, independently and identically distributed (i.i.d), individual fairness, individuals, industry, industry tracks, Inference, innovation, innovative, input function, input layer, instance, Intelligent robots, inter-rater agreement, interpretability, iot, item matrix, items, iteration, JAIR, Journal Sylvie Thiebaux, k-means, k-median, Keras, Kernel Support Vector Machines (KSVMs), label, labeled example, lambda, Large scale analytics, layer, Layers API (tf.layers), leading experts, learning rate, least squares regression, Léon Bottou, liberty, linear model, linear regression, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Lush programming language, Machine ethics, MACHINE LEARNING, Machine learning systems, majority class, Markov decision process (MDP), Markov property, Martha Palmer, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), metric, Metrics API (tf.metrics), Mila, mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), Montreal, moral and ethical use of AI technologies, more complex math (Proximal and others), multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natasha Noy, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, neuron, Neutrality, new moral and ethical challenges, new regulatory, New York University, NLU, node (neural network), node (TensorFlow graph), noise, non-arbitrary, non-response bias, normalization, Nowadays, numerical data, NumPy, objective, objective function, offline inference, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), optimizer, organizing conference, out-group homogeneity bias, outliers, output layer, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, Patrick Haffner, perceptron, performance, perplexity, pipeline, policy, pooling, population, positive class, post-processing, powerful, PR AUC (area under the PR curve), pre-trained model, precision, precision-recall curve, prediction, prediction bias, predictive applications, predictive parity, predictive rate parity, predicts, premade Estimator, preprocessing, principles, prior belief, procedural, process data, Prof. Karine Nahon, Professor of Department of Computer Science and Operations Research, Professors, PropBank, proxy (sensitive attributes), proxy labels, public accountability, public sector, public sectors, Q-function, Q-learning, quantile, quantile bucketing, quantization, Quebec’s AI Institute, queue, Radhika Nagpal, random forest, random policy, rank (ordinality), rank (Tensor), rater, re-ranking, real world, real-world domains, reason, recall, recommendation system, reconstruction, Rectified Linear Unit (ReLU), recurrent neural network, Registration, regression, regression model, regularization, regularization rate, reinforcement learning, reinforcement learning (RL), replay buffer, reporting bias, representation, research and application, research innovations, research track, researchers, respect, Responsibility, results, Retail, return, reward, ridge regularization, RNN, Robot rights, robotics, ROC (receiver operating characteristic) Curve, root directory, Root Mean Squared Error (RMSE), Root Robotics Company, rotational invariance, sampling bias, Satisfying certain basic universal needs, SavedModel, Saver, scalar, scaling, Scientific Director, scikit-learn, scoring, selection bias, semi-supervised learning, sensitive attribute, sequence model, services, serving, session (tf.session), shape (Tensor), sigmoid function, significant capital, similarity measure, size invariance, sketching, society, softmax, sparse feature, sparse representation, sparse vector, sparsity, sparsity/regularization (Ftrl), spatial pooling, sponsorship, squared hinge loss, squared loss, State, state-action value function, state-of-the-art, static model, stationarity, step, step size, stochastic gradient descent (SGD), stride, structural risk minimization (SRM), subsampling, summary, supervised learning, supervised machine learning, support vector machines, synthetic feature, system, Systems for ML, tabular Q-learning, target, target network, technical assistants, technical presentations, TECHNOLOGY, temporal data, Tensor, Tensor Processing Unit (TPU), Tensor rank, Tensor shape, Tensor size, TensorBoard, TensorFlow, TensorFlow Playground, TensorFlow Serving, termination condition, test set, tf.Example, tf.keras, The Association for the Advancement of Artificial Intelligence, The conference, The Institute for Data Valorization, the need of people in AI-based systems, the rights of the individual, The Summit, their benefits, Thirty-Fourth AAAI Conference on Artificial Intelligence, Thomas Schiex, Threat to human dignity, time series analysis, timestep, to benefit, topics, tower, TPU, TPU chip, TPU device, TPU master, TPU node, TPU Pod, TPU resource, TPU slice, TPU type, TPU worker, training, training set, trajectory, transfer learning, translational invariance, transparency, trigram, true negative (TN), true positive (TP), true positive rate (TPR), Turing Award Winners, tutorial, unawareness (to a sensitive attribute), underfitting, Université de Montréal, unlabeled example, unsupervised learning, unsupervised machine learning, update frequency, upweighting, user matrix, validation, validation set, vanishing gradient problem, VerbNet, Wasserstein loss, Weaponization of AI, weight, Weighted Alternating Least Squares (WALS), wide model, width, William T. Freeman, Yann LeCun, Yann LeCun and Yoshua Bengio, Yitzhak Ben-Israel, Yoshua Bengio