- Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki, Oren Freifeld: Diffeomorphic Temporal Alignment Nets.
Diffeomorphic-Temporal-Alignment-Nets
- Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry: A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off.
- Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman: Controlling Neural Level Sets.
Controlling-Neural-Level-Sets
- Alaa Maalouf, Ibrahim Jubran, Dan Feldman: Fast and Accurate Least-Mean-Squares Solvers.
Fast-and-Accurate-Least-Mean-Squares-Solvers
- Sefi Bell-Kligler, Assaf Shocher, Michal Irani: Blind Super-Resolution Kernel Estimation using an Internal-GAN.
Blind-Super-Resolution-Kernel-Estimation-using-an-Internal-GAN
- Yonatan Geifman, Ran El-Yaniv: AI Deep Active Learning with a Neural Architecture Search.
- Gilad Yehudai, Ohad Shamir: On the power and limitations of random features for understanding neural networks.
On-the-power-and-limitations-of-random-features-for-understanding-neural-networks
- Yogev Bar-On, Yishay Mansour: Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits.
Individual-Regret-in-Cooperative-Nonstochastic-Multi-Armed-Bandits
- Eliya Nachmani, Lior Wolf: Hyper-Graph-Network Decoders for Block Codes.
Hyper-Graph-Network-Decoders-for-Block-Codes
- Asaf Noy: XNAS: Neural Architecture Search with Expert Advice.
XNAS-Neural-Architecture-Search-with-Expert-Advice
- Haggai Maron, Heli ben-Hamu, Hadar Serviansky, Yaron Lipman: Provably Powerful Graph Networks.
Provably-Powerful-Graph-Networks
- Dror Simon, Michael Elad: Rethinking the CSC Model for Natural Images.
Rethinking-the-CSC-Model-for-Natural-Images
Tags: "A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off, "Controlling Neural Level Sets, "Diffeomorphic Temporal Alignment Nets", A/B testing, academia, Accuracy, action, activation function, active learning, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI methods, Alaa Maalouf, analytics, analytics space, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, Asaf Noy, Assaf Shocher, association, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, Bellman equation, BGU, bias (ethics/fairness), bias (math), Big Data Analytics, bigram, binary classification, binning, Blind Super-Resolution Kernel Estimation using an Internal-GAN, boosting, broad spectrum, broadcasting, bucketing, calibration layer, 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, Computational neural networks, computer vision, Conference, confirmation bias, confusion matrix, content, Continuous delivery, continuous feature, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, Dan Feldman, Daniel Soudry, Dar Gilboa, data analysis, data augmentation, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), Deci AI Deep Active Learning with a Neural Architecture Search, decision boundary, decision threshold, decision tree, decision trees, deep learning, deep model, deep neural network, Deep Q-Network (DQN), delivering, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, Developers, device, DevOps, Dialogue Bots, dimension reduction, dimensions, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, domains, downsampling, DQN, dropout regularization, Dror Simon, dynamic model, eager execution, early stopping, Education, Eliya Nachmani, embedding space, embeddings, empirical risk minimization (ERM), engineering, Ethics of artificial intelligence, excellent, experience, experimenter’s bias, Explaining of Israel, Facebook, Fast and Accurate Least-Mean-Squares Solvers, Fintech, focus, Future of AI, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, Gilad Yehudai, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great success, greedy policy, ground truth, group attribution bias, Hadar Serviansky, Haggai Maron, hashing, Healthcare, Heli Ben-Hamu, helpful feedback, heuristic, hidden layer, hierarchical clustering, high-quality, hinge loss, holdout data, Hyper-Graph-Network Decoders for Block Codes, hyperparameter, hyperplane, i.i.d., Ibrahim Jubran, ideas, image recognition, imbalanced dataset, implicit bias, improvement, in-group bias, incompatibility of fairness metrics, independently and identically distributed (i.i.d), individual fairness, Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits, industry, industry tracks, Inference, innovation, input function, input layer, instance, Intelligent robots, inter-rater agreement, interpretability, iot, item matrix, items, iteration, 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, linear model, linear regression, Lior Wolf, Lior Yariv, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Machine ethics, MACHINE LEARNING, majority class, Markov decision process (MDP), Markov property, Matan Atzmon, Matan Eyal, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), metric, Metrics API (tf.metrics), Michal Irani, mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), more complex math (Proximal and others), multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, NeurIPS, NeurIPSi, neuron, Nicki Skafte Detlefsen, Niv Haim, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, NVIDIA Research, objective, objective function, Ofer Israelov, offline inference, Ohad Shamir, On the power and limitations of random features for understanding neural networks, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), optimizer, Oren Freifeld, Oren Shriki, organizing conference, out-group homogeneity bias, outliers, output layer, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, perceptron, performance, perplexity, pipeline, policy, pooling, positive class, post-processing, PR AUC (area under the PR curve), predictive applications, Provably Powerful Graph Networks, Ran El-Yaniv, real world, real-world domains, Registration, regression, regularization, reinforcement learning, reporting bias, research and application, research innovations, research track, researchers, Retail, Rethinking the CSC Model for Natural Images, Robot rights, robotics, Ron Shapira Weber, sampling bias, Sefi Bell-Kligler, selection bias, sparsity/regularization (Ftrl), sponsorship, state-of-the-art, Student, supervised learning, support vector machines, Systems for ML, technical presentations, Technion, TECHNOLOGY, Tel Aviv University, The conference, The Summit, The University of Haifa, Threat to human dignity, topics, tutorial, unsupervised learning, update frequency, Weaponization of AI, Weizmann, XNAS: Neural Architecture Search with Expert Advice, Yaniv Blumenfeld, Yaron Lipman, Yishay Mansour, Yogev Bar-On, Yonatan Geifman
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All presentations from the NeurIPSi event TLV19
sherry 0 Data Science, Design, Engineering, Explaining of Israel, Technology,
- Ron Shapira Weber, Matan Eyal, Nicki Skafte Detlefsen, Oren Shriki, Oren Freifeld: Diffeomorphic Temporal Alignment Nets.
Diffeomorphic-Temporal-Alignment-Nets- Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman: Controlling Neural Level Sets.
Controlling-Neural-Level-Sets- Alaa Maalouf, Ibrahim Jubran, Dan Feldman: Fast and Accurate Least-Mean-Squares Solvers.
Fast-and-Accurate-Least-Mean-Squares-Solvers- Sefi Bell-Kligler, Assaf Shocher, Michal Irani: Blind Super-Resolution Kernel Estimation using an Internal-GAN.
Blind-Super-Resolution-Kernel-Estimation-using-an-Internal-GAN- Gilad Yehudai, Ohad Shamir: On the power and limitations of random features for understanding neural networks.
On-the-power-and-limitations-of-random-features-for-understanding-neural-networks- Yogev Bar-On, Yishay Mansour: Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits.
Individual-Regret-in-Cooperative-Nonstochastic-Multi-Armed-Bandits- Eliya Nachmani, Lior Wolf: Hyper-Graph-Network Decoders for Block Codes.
Hyper-Graph-Network-Decoders-for-Block-Codes- Asaf Noy: XNAS: Neural Architecture Search with Expert Advice.
XNAS-Neural-Architecture-Search-with-Expert-Advice- Haggai Maron, Heli ben-Hamu, Hadar Serviansky, Yaron Lipman: Provably Powerful Graph Networks.
Provably-Powerful-Graph-Networks- Dror Simon, Michael Elad: Rethinking the CSC Model for Natural Images.
Rethinking-the-CSC-Model-for-Natural-ImagesTags: "A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off, "Controlling Neural Level Sets, "Diffeomorphic Temporal Alignment Nets", A/B testing, academia, Accuracy, action, activation function, active learning, AdaGrad, administrative perspectives, agent, agglomerative clustering, AI, AI methods, Alaa Maalouf, analytics, analytics space, applied machine learning, ar, area under the PR curve, area under the ROC curve, artificial general intelligence, artificial intelligence, Asaf Noy, Assaf Shocher, association, attribute, AUC (Area under the ROC Curve), Augmented Reality, automation bias, Automation of Machine-Learning, Autonomous driving, average precision, backpropagation, bag of words, baseline, batch, batch normalization, batch size, Bayesian, Bayesian neural network, Bellman equation, BGU, bias (ethics/fairness), bias (math), Big Data Analytics, bigram, binary classification, binning, Blind Super-Resolution Kernel Estimation using an Internal-GAN, boosting, broad spectrum, broadcasting, bucketing, calibration layer, 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, Computational neural networks, computer vision, Conference, confirmation bias, confusion matrix, content, Continuous delivery, continuous feature, convenience sampling, convergence, convex function, convex optimization, convex set, convolution, convolutional filter, convolutional layer, convolutional layers, convolutional neural network, convolutional operation, cost, counterfactual fairness, coverage bias, crash blossom, critic, Cross validation, cross-entropy, custom Estimator, Cyber, Dan Feldman, Daniel Soudry, Dar Gilboa, data analysis, data augmentation, Data Science, Data Scientists, data set or dataset, database, DataFrame, Dataset API (tf.data), Deci AI Deep Active Learning with a Neural Architecture Search, decision boundary, decision threshold, decision tree, decision trees, deep learning, deep model, deep neural network, Deep Q-Network (DQN), delivering, demographic parity, dense feature, dense layer, depth, depthwise separable convolutional neural network (sepCNN), DESIGN, Developers, device, DevOps, Dialogue Bots, dimension reduction, dimensions, discrete feature, discriminative model, discriminator, disparate impact, disparate treatment, divisive clustering, domains, downsampling, DQN, dropout regularization, Dror Simon, dynamic model, eager execution, early stopping, Education, Eliya Nachmani, embedding space, embeddings, empirical risk minimization (ERM), engineering, Ethics of artificial intelligence, excellent, experience, experimenter’s bias, Explaining of Israel, Facebook, Fast and Accurate Least-Mean-Squares Solvers, Fintech, focus, Future of AI, GAN, generalization, generalization curve, generalized linear model, generative adversarial network (GAN), generative model, generator, Gilad Yehudai, gradient, gradient clipping, gradient descent, Gradient descent algorithm, graph, graph execution, great success, greedy policy, ground truth, group attribution bias, Hadar Serviansky, Haggai Maron, hashing, Healthcare, Heli Ben-Hamu, helpful feedback, heuristic, hidden layer, hierarchical clustering, high-quality, hinge loss, holdout data, Hyper-Graph-Network Decoders for Block Codes, hyperparameter, hyperplane, i.i.d., Ibrahim Jubran, ideas, image recognition, imbalanced dataset, implicit bias, improvement, in-group bias, incompatibility of fairness metrics, independently and identically distributed (i.i.d), individual fairness, Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits, industry, industry tracks, Inference, innovation, input function, input layer, instance, Intelligent robots, inter-rater agreement, interpretability, iot, item matrix, items, iteration, 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, linear model, linear regression, Lior Wolf, Lior Yariv, Log Loss, log-odds, logistic regression, logits, Long Short-Term Memory (LSTM), loss, loss curve, loss surface, LSTM, Machine ethics, MACHINE LEARNING, majority class, Markov decision process (MDP), Markov property, Matan Atzmon, Matan Eyal, matplotlib, matrix factorization, Mean Absolute Error (MAE), Mean Squared Error (MSE), metric, Metrics API (tf.metrics), Michal Irani, mini-batch, mini-batch stochastic gradient descent (SGD), minimax loss, minority class, ML, MNIST, model, model function, model training, Momentum, momentum (Momentum), more complex math (Proximal and others), multi-class classification, multi-class logistic regression, multi-class regression, multinomial classification, N-gram, NaN trap, Natural language processing, Natural Language Understanding, negative class, neural network, neural networks, NeurIPS, NeurIPSi, neuron, Nicki Skafte Detlefsen, Niv Haim, NLU, node (neural network), node (TensorFlow graph), noise, non-response bias, normalization, numerical data, NumPy, NVIDIA Research, objective, objective function, Ofer Israelov, offline inference, Ohad Shamir, On the power and limitations of random features for understanding neural networks, one-hot encoding, one-shot learning, one-vs.-all, online inference, Operation (op), optimizer, Oren Freifeld, Oren Shriki, organizing conference, out-group homogeneity bias, outliers, output layer, Overfitting, pandas, parameter, Parameter Server (PS), parameter update, partial derivative, participants, participation bias, partitioning strategy, perceptron, performance, perplexity, pipeline, policy, pooling, positive class, post-processing, PR AUC (area under the PR curve), predictive applications, Provably Powerful Graph Networks, Ran El-Yaniv, real world, real-world domains, Registration, regression, regularization, reinforcement learning, reporting bias, research and application, research innovations, research track, researchers, Retail, Rethinking the CSC Model for Natural Images, Robot rights, robotics, Ron Shapira Weber, sampling bias, Sefi Bell-Kligler, selection bias, sparsity/regularization (Ftrl), sponsorship, state-of-the-art, Student, supervised learning, support vector machines, Systems for ML, technical presentations, Technion, TECHNOLOGY, Tel Aviv University, The conference, The Summit, The University of Haifa, Threat to human dignity, topics, tutorial, unsupervised learning, update frequency, Weaponization of AI, Weizmann, XNAS: Neural Architecture Search with Expert Advice, Yaniv Blumenfeld, Yaron Lipman, Yishay Mansour, Yogev Bar-On, Yonatan Geifman
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