ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Paper content:
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers.
Our model builds an object-based scene representation and translates sentences into executable, symbolic programs.
To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.
Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to.
Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences.
We use curriculum learning to guide the searching over the large compositional space of images and language.
Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains.
It also empowers applications including visual question answering and bidirectional image-text retrieval.

assistant: text: We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fast Task Inference with Variational Intrinsic Successor Features

Paper content:
It has been established that diverse behaviors spanning the controllable subspace of a Markov decision process can be trained by rewarding a policy for being distinguishable from other policies.
However, one limitation of this formulation is the difficulty to generalize beyond the finite set of behaviors being explicitly learned, as may be needed in subsequent tasks.
Successor features provide an appealing solution to this generalization problem, but require defining the reward function as linear in some grounded feature space.
In this paper, we show that these two techniques can be combined, and that each method solves the other's primary limitation.
To do so we introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide enhanced generalization and fast task inference through the successor features framework.
We empirically validate VISR on the full Atari suite, in a novel setup wherein the rewards are only exposed briefly after a long unsupervised phase.
Achieving human-level performance on 12 games and beating all baselines, we believe VISR represents a step towards agents that rapidly learn from limited feedback.

assistant: text: We introduce Variational Intrinsic Successor FeatuRes (VISR), a novel algorithm which learns controllable features that can be leveraged to provide fast task inference through the successor features framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Continual Learning

Paper content:
This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks.
The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge.
Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.

assistant: text: This paper develops a principled method for continual learning in deep models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Synonym Expansion for Large Shopping Taxonomies

Paper content:
We present an approach for expanding taxonomies with synonyms, or aliases.
We target large shopping taxonomies, with thousands of nodes.
A comprehensive set of entity aliases is an important component of identifying entities in unstructured text such as product reviews or search queries.
Our method consists of two stages: we generate synonym candidates from WordNet and shopping search queries, then use a binary classiﬁer to ﬁlter candidates.
We process taxonomies with thousands of synonyms in order to generate over 90,000 synonyms.
We show that using the taxonomy to derive contextual features improves classiﬁcation performance over using features from the target node alone.We show that our approach has potential for transfer learning between diﬀerent taxonomy domains, which reduces the need to collect training data for new taxonomies.

assistant: text: We use machine learning to generate synonyms for large shopping taxonomies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Anomalous Pattern Detection in Activations and Reconstruction Error of Autoencoders

Paper content:
In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely.
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.
Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalous if the reconstruction error exceeds some threshold.
In this paper, we proposed an unsupervised method based on subset scanning over autoencoder activations.
The contributions of our work are threefold.
First, we propose a novel method combining detection with reconstruction error and subset scanning scores to improve the anomaly score of current autoencoders without requiring any retraining.
Second, we provide the ability to inspect and visualize the set of anomalous nodes in the reconstruction error space that make a sample noised.
Third, we show that subset scanning can be used for anomaly detection in the inner layers of the autoencoder.
We provide detection power results for several untargeted adversarial noise models under standard datasets.

assistant: text: Unsupervised method to detect adversarial samples in autoencoder's activations and reconstruction error space
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exploring the Space of Black-box Attacks on Deep Neural Networks

Paper content:
Existing black-box attacks on deep neural networks (DNNs) so far have largely focused on transferability, where an adversarial instance generated for a locally trained model can “transfer” to attack other learning models.
In this paper, we propose novel Gradient Estimation black-box attacks for adversaries with query access to the target model’s class probabilities, which do not rely on transferability.
We also propose strategies to decouple the number of queries required to generate each adversarial sample from the dimensionality of the input.
An iterative variant of our attack achieves close to 100% adversarial success rates for both targeted and untargeted attacks on DNNs.
We carry out extensive experiments for a thorough comparative evaluation of black-box attacks and show that the proposed Gradient Estimation attacks outperform all transferability based black-box attacks we tested on both MNIST and CIFAR-10 datasets, achieving adversarial success rates similar to well known, state-of-the-art white-box attacks.
We also apply the Gradient Estimation attacks successfully against a real-world content moderation classiﬁer hosted by Clarifai.
Furthermore, we evaluate black-box attacks against state-of-the-art defenses.
We show that the Gradient Estimation attacks are very effective even against these defenses.

assistant: text: Query-based black-box attacks on deep neural networks with adversarial success rates matching white-box attacks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Predicted Variables in Programming

Paper content:
We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.
There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand.
PVars aim to make using ML in programming easier by hybridizing the two.
We leverage the existing concept of variables and create a new type, a predicted variable.
PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated.
We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.
We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.
As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced.
PVars allow for a seamless integration of ML into existing systems and algorithms.
Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods.
To learn faster, PVars use the heuristic function, which they are replacing, as an initial function.
We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.

assistant: text: We present Predicted Variables, an approach to making machine learning a first class citizen in programming languages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Differentially Private Survival Function Estimation

Paper content:
Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator.
Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern.
We propose a first differentially private estimator of the survival function and show that it can be easily extended to provide differentially private confidence intervals and test statistics without spending any extra privacy budget.
We further provide extensions for differentially private estimation of the competing risk cumulative incidence function.
Using nine real-life clinical datasets, we provide empirical evidence that our proposed method provides good utility while simultaneously providing strong privacy guarantees.

assistant: text: A first differentially private estimate of the survival function
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping

Paper content:
We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature.
First, we train a neural NER (NNER) model over a small seed of fully-labeled examples.
Second, we use a reference set of entity names (e.g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.
Third, we use the NNER model to assign weak labels to the corpus.
Finally, we retrain our NNER model iteratively over the augmented training set, including the seed, the reference-set examples, and the weakly-labeled examples, which results in refined labels.
We show empirically that this augmented bootstrapping process significantly improves NER performance, and discuss the factors impacting the efficacy of the approach.

assistant: text: Augmented bootstrapping approach combining information from a reference set with iterative refinements of soft labels to improve Name Entity Recognition from biomedical literature.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: VILD: Variational Imitation Learning with Diverse-quality Demonstrations

Paper content:
The goal of imitation learning (IL) is to learn a good policy from high-quality demonstrations.
However, the quality of demonstrations in reality can be diverse, since it is easier and cheaper to collect demonstrations from a mix of experts and amateurs.
IL in such situations can be challenging, especially when the level of demonstrators' expertise is unknown.
We propose a new IL paradigm called Variational Imitation Learning with Diverse-quality demonstrations (VILD), where we explicitly model the level of demonstrators' expertise with a probabilistic graphical model and estimate it along with a reward function.
We show that a naive estimation approach is not suitable to large state and action spaces, and fix this issue by using a variational approach that can be easily implemented using existing reinforcement learning methods.
Experiments on continuous-control benchmarks demonstrate that VILD outperforms state-of-the-art methods.
Our work enables scalable and data-efficient IL under more realistic settings than before.

assistant: text: We propose an imitation learning method to learn from diverse-quality demonstrations collected by demonstrators with different level of expertise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: pix2code: Generating Code from a Graphical User Interface Screenshot

Paper content:
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.
In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).

assistant: text: CNN and LSTM to generate markup-like code describing graphical user interface images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PolyGAN: High-Order Polynomial Generators

Paper content:
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.
Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures.
In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators.
Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors.
We introduce two tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks that only employ linear/convolutional blocks.
We exhibit for the first time that by using our approach a GAN generator can approximate the data distribution without using any activation functions.
Thorough experimental evaluation on both synthetic and real data (images and 3D point clouds) demonstrates the merits of PolyGAN against the state of the art.

assistant: text: We model the data generator (in GAN) by means of a high-order polynomial represented by high-order tensors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation

Paper content:
Open-domain dialogue generation has gained increasing attention in Natural Language Processing.
Comparing these methods requires a holistic means of dialogue evaluation.
Human ratings are deemed as the gold standard.
As human evaluation is inefficient and costly, an automated substitute is desirable.
In this paper, we propose holistic evaluation metrics which capture both the quality and diversity of dialogues.
Our metrics consists of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, and, (3) $n$-gram based diversity in responses to augmented queries.
The empirical validity of our metrics is demonstrated by strong correlation with human judgments.
We provide the associated code, datasets and human ratings.

assistant: text: We propose automatic metrics to holistically evaluate open-dialogue generation and they strongly correlate with human evaluation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual Gated Graph ConvNets

Paper content:
Graph-structured data such as social networks, functional brain networks, gene regulatory networks, communications networks have brought the interest in generalizing deep learning techniques to graph domains.
In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.
Most existing works have focused on recurrent neural networks (RNNs) to learn meaningful representations of graphs, and more recently new convolutional neural networks (ConvNets) have been introduced.
In this work, we want to compare rigorously these two fundamental families of architectures to solve graph learning tasks.
We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size.
Then, we design a set of analytically controlled experiments on two basic graph problems, i.e. subgraph matching and graph clustering, to test the different architectures.
Numerical results show that the proposed graph ConvNets are 3-17% more accurate and 1.5-4x faster than graph RNNs.
Graph ConvNets are also 36% more accurate than variational (non-learning) techniques.
Finally, the most effective graph ConvNet architecture uses gated edges and residuality.
Residuality plays an essential role to learn multi-layer architectures as they provide a 10% gain of performance.

assistant: text: We compare graph RNNs and graph ConvNets, and we consider the most generic class of graph ConvNets with residuality.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Omega: An Architecture for AI Unification

Paper content:
We introduce the open-ended, modular, self-improving Omega AI unification architecture which is a refinement of Solomonoff's Alpha architecture, as considered from first principles.
The architecture embodies several crucial principles of general intelligence including diversity of representations, diversity of data types, integrated memory, modularity, and higher-order cognition.
We retain the basic design of a fundamental algorithmic substrate called an ``AI kernel'' for problem solving and basic cognitive functions like memory, and a larger, modular architecture that re-uses the kernel in many ways.
Omega includes eight representation languages and six classes of neural networks, which are briefly introduced.
The architecture is intended to initially address data science automation, hence it includes many problem solving methods for statistical tasks.
We review the broad software architecture, higher-order cognition, self-improvement, modular neural architectures, intelligent agents, the process and memory hierarchy, hardware abstraction, peer-to-peer computing, and data abstraction facility.

assistant: text: It's a new AGI architecture for trans-sapient performance.This is a high-level overview of the Omega AGI architecture which is the basis of a data science automation system. Submitted to a workshop.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Phase Transitions for the Information Bottleneck in Representation Learning

Paper content:
In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation?
In this paper, we set out to answer these questions by studying multiple phase transitions in the IB objective: IB_β[p(z|x)] = I(X; Z) − βI(Y; Z) defined on the encoding distribution p(z|x) for input X, target Y and representation Z, where sudden jumps of dI(Y; Z)/dβ and prediction accuracy are observed with increasing β.
We introduce a definition for IB phase transitions as a qualitative change of the IB loss landscape, and show that the transitions correspond to the onset of learning new classes.
Using second-order calculus of variations, we derive a formula that provides a practical condition for IB phase transitions, and draw its connection with the Fisher information matrix for parameterized models.
We provide two perspectives to understand the formula, revealing that each IB phase transition is finding a component of maximum (nonlinear) correlation between X and Y orthogonal to the learned representation, in close analogy with canonical-correlation analysis (CCA) in linear settings.
Based on the theory, we present an algorithm for discovering phase transition points.
Finally, we verify that our theory and algorithm accurately predict phase transitions in categorical datasets, predict the onset of learning new classes and class difficulty in MNIST, and predict prominent phase transitions in CIFAR10.

assistant: text: We give a theoretical analysis of the Information Bottleneck objective to understand and predict observed phase transitions in the prediction vs. compression tradeoff.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Energy Dissipation with Plug-and-Play Priors

Paper content:
Neural networks have reached outstanding performance for solving various ill-posed inverse problems in imaging.
However, drawbacks of end-to-end learning approaches in comparison to classical variational methods are the requirement of expensive retraining for even slightly different problem statements and the lack of provable error bounds during inference.
Recent works tackled the first problem by using networks trained for Gaussian image denoising as generic plug-and-play regularizers in energy minimization algorithms.
Even though this obtains state-of-the-art results on many tasks, heavy restrictions on the network architecture have to be made if provable convergence of the underlying fixed point iteration is a requirement.
More recent work has proposed to train networks to output descent directions with respect to a given energy function with a provable guarantee of convergence to a minimizer of that energy.
However, each problem and energy requires the training of a separate network.
In this paper we consider the combination of both approaches by projecting the outputs of a plug-and-play denoising network onto the cone of descent directions to a given energy.
This way, a single pre-trained network can be used for a wide variety of reconstruction tasks.
Our results show improvements compared to classical energy minimization methods while still having provable convergence guarantees.

assistant: text: We use neural networks trained for image denoising as plug-and-play priors in energy minimization algorithms for image reconstruction problems with provable convergence.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Existing neural question answering (QA) models are required to reason over and draw complicated inferences from a long context for most large-scale QA datasets.
However, if we view QA as a combined retrieval and reasoning task, we can assume the existence of a minimal context which is necessary and sufficient to answer a given question.
Recent work has shown that a sentence selector module that selects a shorter context and feeds it to the downstream QA model achieves performance comparable to a QA model trained on full context, while also being more interpretable.
Recent work has also shown that most state-of-the-art QA models break when adversarially generated sentences are appended to the context.
While humans are immune to such distractor sentences, QA models get easily misled into selecting answers from these sentences.
We hypothesize that the sentence selector module can filter out extraneous context, thereby allowing the downstream QA model to focus and reason over the parts of the context that are relevant to the question.
In this paper, we show that the sentence selector itself is susceptible to adversarial inputs.
However, we demonstrate that a pipeline consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context.
Thus, we provide evidence towards a modular approach for question answering that is more robust and interpretable.

assistant: text: A modular approach consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Total Style Transfer with a Single Feed-Forward Network

Paper content:
Recent image style transferring methods achieved arbitrary stylization with input content and style images.
To transfer the style of an arbitrary image to a content image, these methods used a feed-forward network with a lowest-scaled feature transformer or a cascade of the networks with a feature transformer of a corresponding scale.
However, their approaches did not consider either multi-scaled style in their single-scale feature transformer or dependency between the transformed feature statistics across the cascade networks.
This shortcoming resulted in generating partially and inexactly transferred style in the generated images.
To overcome this limitation of partial style transfer, we propose a total style transferring method which transfers multi-scaled feature statistics through a single feed-forward process.
First, our method transforms multi-scaled feature maps of a content image into those of a target style image by considering both inter-channel correlations in each single scaled feature map and inter-scale correlations between multi-scaled feature maps.
Second, each transformed feature map is inserted into the decoder layer of the corresponding scale using skip-connection.
Finally, the skip-connected multi-scaled feature maps are decoded into a stylized image through our trained decoder network.

assistant: text: A paper suggesting a method to transform the style of images using deep neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Certifying Neural Network Audio Classifiers

Paper content:
We present the first end-to-end verifier of audio classifiers.
Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM).
The audio processing is verified using novel convex relaxations tailored to feature extraction operations used in audio (e.g., Fast Fourier Transform) while recurrent architectures are certified via a novel binary relaxation for the recurrent unit update.
We show the verifier scales to large networks while computing significantly tighter bounds than existing methods for common audio classification benchmarks: on the challenging Google Speech Commands dataset we certify 95% more inputs than the interval approximation (only prior scalable method), for a perturbation of -90dB.

assistant: text: We present the first approach to certify robustness of neural networks against noise-based perturbations in the audio domain.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Dynamic Approach to Accelerate Deep Learning Training

Paper content:
Mixed-precision arithmetic combining both single- and half-precision operands in the same operation have been successfully applied to train deep neural networks.
Despite the advantages of mixed-precision arithmetic in terms of reducing the need for key resources like memory bandwidth or register file size, it has a limited capacity for diminishing computing costs and requires 32 bits to represent its output operands.
This paper proposes two approaches to replace mixed-precision for half-precision arithmetic during a large portion of the training.
The first approach achieves accuracy ratios slightly slower than the state-of-the-art by using half-precision arithmetic during more than 99% of training.
The second approach reaches the same accuracy as the state-of-the-art by dynamically switching between half- and mixed-precision arithmetic during training.
It uses half-precision during more than 94% of the training process.
This paper is the first in demonstrating that half-precision can be used for a very large portion of DNNs training and still reach state-of-the-art accuracy.

assistant: text: Dynamic precision technique to train deep neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: HiLLoC: lossless image compression with hierarchical latent variable models

Paper content:
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.
We use this property, applying fully convolutional models to lossless compression, demonstrating a method to scale the VAE-based 'Bits-Back with ANS' algorithm for lossless compression to large color photographs, and achieving state of the art for compression of full size ImageNet images.
We release Craystack, an open source library for convenient prototyping of lossless compression using probabilistic models, along with full implementations of all of our compression results.

assistant: text: We scale up lossless compression with latent variables, beating existing approaches on full-size ImageNet images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Critical Points of Linear Neural Networks: Analytical Forms and Landscape Properties

Paper content:
Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural networks from a theoretical aspect.
Particularly, the properties of critical points and the landscape around them are of importance to determine the convergence performance of optimization algorithms.
In this paper, we provide a necessary and sufficient characterization of the analytical forms for the critical points (as well as global minimizers) of the square loss functions for linear neural networks.
We show that the analytical forms of the critical points characterize the values of the corresponding loss functions as well as the necessary and sufficient conditions to achieve global minimum.
Furthermore, we exploit the analytical forms of the critical points to characterize the landscape properties for the loss functions of linear neural networks and shallow ReLU networks.
One particular conclusion is that: While the loss function of linear networks has no spurious local minimum, the loss function of one-hidden-layer nonlinear networks with ReLU activation function does have local minimum that is not global minimum.

assistant: text: We provide necessary and sufficient analytical forms for the critical points of the square loss functions for various neural networks, and exploit the analytical forms to characterize the landscape properties for the loss functions of these neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters

Paper content:
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.
Consequently, model size reduction has become an utmost goal in deep learning.
A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes.
However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates.
In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution.
By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set.
The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family.
Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance.

assistant: text: This paper proposes an effective method to compress neural networks based on recent results in information theory.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CATER: A diagnostic dataset for Compositional Actions & TEmporal Reasoning

Paper content:
Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets.
However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements.
Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive.
We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure.
In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.
Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning.
In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable.
Using CATER, we provide insights into some of the most recent state of the art deep video architectures.

assistant: text: We propose a new video understanding benchmark, with tasks that by-design require temporal reasoning to be solved, unlike most existing video datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Image Segmentation by Iterative Inference from Conditional Score Estimation

Paper content:
Inspired by the combination of feedforward and iterative computations in the visual cortex, and taking advantage of the ability of denoising autoencoders to estimate the score of a joint distribution, we propose a novel approach to iterative inference for capturing and exploiting the complex joint distribution of output variables conditioned on some input variables.
This approach is applied to image pixel-wise segmentation, with the estimated conditional score used to perform gradient ascent towards a mode of the estimated conditional distribution.
This extends previous work on score estimation by denoising autoencoders to the case of a conditional distribution, with a novel use of a corrupted feedforward predictor replacing Gaussian corruption.
An advantage of this approach over more classical ways to perform iterative inference for structured outputs, like conditional random fields (CRFs), is that it is not any more necessary to define an explicit energy function linking the output variables.
To keep computations tractable, such energy function parametrizations are typically fairly constrained, involving only a few neighbors of each of the output variables in each clique.
We experimentally find that the proposed iterative inference from conditional score estimation by conditional denoising autoencoders performs better than comparable models based on CRFs or those not using any explicit modeling of the conditional joint distribution of outputs.

assistant: text: Refining segmentation proposals by performing iterative inference with conditional denoising autoencoders.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Engaging Image Captioning Via Personality

Paper content:
Standard image captioning tasks such as COCO and Flickr30k are factual, neutral in tone and (to a human) state the obvious (e.g., “a man playing a guitar”).
While such tasks are useful to verify that a machine understands the content of an image,  they are not engaging to humans as captions.
With this in mind we define a new task, Personality-Captions, where the goal is to be as engaging to humans as possible by incorporating controllable style and personality traits.
We collect and release a large dataset of 201,858 of such captions conditioned over 215 possible traits.
We build models that combine existing work from (i) sentence representations (Mazaré et al., 2018) with Transformers trained on 1.7 billion dialogue examples; and (ii) image representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion social media images.
We obtain state-of-the-art performance on Flickr30k and COCO, and strong performance on our new task.
Finally, online evaluations validate that our task and models are engaging to humans, with our best model close to human performance.

assistant: text: We develop engaging image captioning models conditioned on personality that are also state of the art on regular captioning tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Neural Abstractive Summarization Using Transfer Learning and Factuality-Based Evaluation: Towards Automating Science Journalism

Paper content:
We propose Automating Science Journalism (ASJ), the process of producing a press release from a scientific paper, as a novel task that can serve as a new benchmark for neural abstractive summarization.
ASJ is a challenging task as it requires long source texts to be summarized to long target texts, while also paraphrasing complex scientific concepts to be understood by the general audience.
For this purpose, we introduce a specialized dataset for ASJ that contains scientific papers and their press releases from Science Daily.
While state-of-the-art sequence-to-sequence (seq2seq) models could easily generate convincing press releases for ASJ, these are generally nonfactual and deviate from the source.
To address this issue, we improve seq2seq generation via transfer learning by co-training with new targets:
(i) scientific abstracts of sources and
(ii) partitioned press releases.
We further design a measure for factuality that scores how pertinent to the scientific papers the press releases under our seq2seq models are.
Our quantitative and qualitative evaluation shows sizable improvements over a strong baseline, suggesting that the proposed framework could improve seq2seq summarization beyond ASJ.

assistant: text: New: application of seq2seq modelling to automating sciene journalism; highly abstractive dataset; transfer learning tricks; automatic evaluation measure.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Structured Deep Factorization Machine: Towards General-Purpose Architectures

Paper content:
In spite of their great success, traditional factorization algorithms typically do not support features (e.g., Matrix Factorization), or their complexity scales quadratically with the number of features (e.g, Factorization Machine).
On the other hand, neural methods allow large feature sets, but are often designed for a specific application.
We propose novel deep factorization methods that allow efficient and flexible feature representation.
For example, we enable describing items with natural language with complexity linear to the vocabulary size—this enables prediction for unseen items and avoids the cold start problem.
We show that our architecture can generalize some previously published single-purpose neural architectures.
Our experiments suggest improved training times and accuracy compared to shallow methods.

assistant: text: Scalable general-purpose factorization algorithm-- also helps to circumvent cold start problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SOM-VAE: Interpretable Discrete Representation Learning on Time Series

Paper content:
High-dimensional time series are common in many domains.
Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations.
However, most representation learning algorithms for time series data are difficult to interpret.
This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.
To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling.
This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance.
We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original.
Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.
This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.
We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.
Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.

assistant: text: We present a method to learn interpretable representations on time series using ideas from variational autoencoders, self-organizing maps and probabilistic models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision

Paper content:
A major challenge in learning image representations is the disentangling of the factors of variation underlying the image formation.
This is typically achieved with an autoencoder architecture where a subset of the latent variables is constrained to correspond to specific factors, and the rest of them are considered nuisance variables.
This approach has an important drawback: as the dimension of the nuisance variables is increased, image reconstruction is improved, but the decoder has the flexibility to ignore the specified factors, thus losing the ability to condition the output on them.
In this work, we propose to overcome this trade-off by progressively growing the dimension of the latent code, while constraining the Jacobian of the output image with respect to the disentangled variables to remain the same.
As a result, the obtained models are effective at both disentangling and reconstruction.
We demonstrate the applicability of this method in both unsupervised and supervised scenarios for learning disentangled representations.
In a facial attribute manipulation task, we obtain high quality image generation while smoothly controlling dozens of attributes with a single model.
This is an order of magnitude more disentangled factors than state-of-the-art methods, while obtaining visually similar or superior results, and avoiding adversarial training.

assistant: text: A method for learning image representations that are good for both disentangling factors of variation and obtaining faithful reconstructions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Multi-facet Embeddings of Phrases and Sentences using Sparse Coding for Unsupervised Semantic Applications

Paper content:
Most deep learning for NLP represents each word with a single point or single-mode region in semantic space, while the existing multi-mode word embeddings cannot represent longer word sequences like phrases or sentences.
We introduce a phrase representation (also applicable to sentences) where each phrase has a distinct set of multi-mode codebook embeddings to capture different semantic facets of the phrase's meaning.
The codebook embeddings can be viewed as the cluster centers which summarize the distribution of possibly co-occurring words in a pre-trained word embedding space.
We propose an end-to-end trainable neural model that directly predicts the set of cluster centers from the input text sequence (e.g., a phrase or a sentence) during test time.
We find that the per-phrase/sentence codebook embeddings not only provide a more interpretable semantic representation but also outperform strong baselines (by a large margin in some tasks) on benchmark datasets for unsupervised phrase similarity, sentence similarity, hypernym detection, and extractive summarization.

assistant: text: We propose an unsupervised way to learn multiple embeddings for sentences and phrases