An official #PyTorch implementation of “Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation” (#NeurIPS 2019) by Risto Vuorio*, Shao-Hua Sun*, Hexiang Hu, and Joseph J. Lim

This project is an implementation of Multimodal Model-Agnostic #MetaLearning via Task-Aware Modulation, which is published in NeurIPS 2019. Please contact Shao-Hua Sun for any questions.

Model-agnostic meta-learners aim to acquire meta-prior parameters from a distribution of tasks and adapt to novel tasks with few gradient updates. Yet, seeking a common initialization shared across the entire task distribution substantially limits the diversity of the task distributions that they are able to learn from. We propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior according to the identified mode, allowing more efficient fast adaptation. An illustration of the proposed framework is as follows.

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#Text2FaceGAN: Face Generation from Fine Grained Textual Denoscriptions

Powerful #GenerativeAdversarialNetworks ( 3GAN) have been developed to automatically synthesize realistic images from text. However, most existing tasks are limited to generating simple images such as flowers from captions. In this work, we extend this problem to the less addressed domain of face generation from fine-grained textual denoscriptions of face, e.g., "A person has curly hair, oval face, and mustache". We are motivated by the potential of automated face generation to impact and assist critical tasks such as criminal face reconstruction. Since current datasets for the task are either very small or do not contain captions, we generate captions for images in the CelebA dataset by creating an algorithm to automatically convert a list of attributes to a set of captions. We then model the highly multi-modal problem of text to face generation as learning the conditional distribution of faces (conditioned on text) in same latent space. We utilize the current state-of-the-art GAN (DC-GAN with GAN-CLS loss) for learning conditional multi-modality. The presence of more fine-grained details and variable length of the captions makes the problem easier for a user but more difficult to handle compared to the other text-to-image tasks. We flipped the labels for real and fake images and added noise in discriminator. Generated images for diverse textual denoscriptions show promising results. In the end, we show how the widely used inceptions score is not a good metric to evaluate the performance of generative models used for synthesizing faces from text.

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#DBSN: Measuring Uncertainty through #Bayesian Learning of #DeepNeuralNetwork Structures

Bayesian neural networks (BNNs) introduce uncertainty estimation to #deep networks by performing Bayesian inference on network weights. However, such models bring the challenges of inference, and further BNNs with weight uncertainty rarely achieve superior performance to standard models. In this paper, we investigate a new line of Bayesian deep learning by performing Bayesian reasoning on the structure of deep neural networks. Drawing inspiration from the neural architecture search, we define the network structure as gating weights on the redundant operations between computational nodes, and apply stochastic variational inference techniques to learn the structure distributions of networks. Empirically, the proposed method substantially surpasses the advanced deep neural networks across a range of classification and segmentation tasks. More importantly, our approach also preserves benefits of Bayesian principles, producing improved uncertainty estimation than the strong baselines including MC dropout and variational #BNNs algorithms (e.g. noisy EK-FAC).

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Pieter #Abbeel - Towards #AI for the Physical World

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#Fun
#Tesla
#Relu

Ref: deeplearning.ai

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Algorithmic Improvements for #DeepReinforcement #Learning applied to Interactive Fiction

Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.

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Price Tags in Comparison

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My #Pandas Cheat Sheet

An early Christmas present for my fellow Data Scientists

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#Google Introduces New Metrics for #AI -Generated Audio and Video Quality

Google AI researchers published two new metrics for measuring the quality of audio and video generated by deep-learning networks, the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). The metrics have been shown to have a high correlation with human evaluations of quality.

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December Edition: Towards Data Science Most Influential Articles This Year

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Introducing TensorBoard.dev: a new way to share your #ML experiment results

TensorBoard, TensorFlow’s visualization toolkit, is often used by researchers and engineers to visualize and understand their ML experiments. It enables tracking experiment metrics, visualizing models, profiling ML programs, visualizing hyperparameter tuning experiments, and much more.

#TensorBoard

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Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.

#OpenAI

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Major trends in #NLP : a review of 20 years of #ACL research

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and — guess what — NLP-based analysis of ACL papers from the last 20 years

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Generalized Coefficient of Correlation for Non-Linear Relationships

What is the best correlation coefficient R(X, Y) to measure non-linear dependencies between two variables X and Y? Let's say that you want to assess weather there is a linear or quadratic relationship between X and Y. One way to do it is to perform a polynomial regression such as Y = a + bX + cX^2, and then measure the standard coefficient of correlation between the predicted and observed values. How good is this approach?

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#Fun

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DeepLearning Academy courses

Applied Deep Learning for Predictive Analytics

Deep Learning with #TensorFlow

#DeepLearning
#Course

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#DeepLearning models tend to increase their accuracy with the increasing amount of training data, where’s traditional #MachineLearning models such as #SVM and Naive #Bayes classifier stop improving after a saturation point.

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#VariationalAutoencoder Theory

The Variational Autoencoder has taken the #MachineLearning community by storm since Kingma and Welling’s seminal paper was released in 20131.

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#DecisionTree vs #RandomForest vs #GradientBoostingMachines: Explained Simply

Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:

* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.

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Semantic Image #Segmentation with #DeepLab in #TensorFlow

Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.

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