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

Title: Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation

Paper content:
Convolutional networks are not aware of an object's geometric variations, which leads to inefficient utilization of model and data capacity.
To overcome this issue, recent works on deformation modeling seek to spatially reconfigure the data towards a common arrangement such that semantic recognition suffers less from deformation.
This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field.
Yet adapting the receptive field does not quite reach the actual goal -- what really matters to the network is the *effective* receptive field (ERF), which reflects how much each pixel contributes.
It is thus natural to design other approaches to adapt the ERF directly during runtime.
In this work, we instantiate one possible solution as Deformable Kernels (DKs), a family of novel and generic convolutional operators for handling object deformations by directly adapting the ERF while leaving the receptive field untouched.
At the heart of our method is the ability to resample the original kernel space towards recovering the deformation of objects.
This approach is justified with theoretical insights that the ERF is strictly determined by data sampling locations and kernel values.
We implement DKs as generic drop-in replacements of rigid kernels and conduct a series of empirical studies whose results conform with our theories.
Over several tasks and standard base models, our approach compares favorably against prior works that adapt during runtime.
In addition, further experiments suggest a working mechanism orthogonal and complementary to previous works.

assistant: text: Don't deform your convolutions -- deform your kernels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ResBinNet: Residual Binary Neural Network

Paper content:
Recent efforts on training light-weight binary neural networks offer promising execution/memory efficiency.
This paper introduces ResBinNet, which is a composition of two interlinked methodologies aiming to address the slow convergence speed and limited accuracy of binary convolutional neural networks.
The first method, called residual binarization, learns a multi-level binary representation for the features within a certain neural network layer.
The second method, called temperature adjustment, gradually binarizes the weights of a particular layer.
The two methods jointly learn a set of soft-binarized parameters that improve the convergence rate and accuracy of binary neural networks.
We corroborate the applicability and scalability of ResBinNet by implementing a prototype hardware accelerator.
The accelerator is reconfigurable in terms of the numerical precision of the binarized features, offering a trade-off between runtime and inference accuracy.

assistant: text: Residual Binary Neural Networks significantly improve the convergence rate and inference accuracy of the binary neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A comprehensive, application-oriented study of catastrophic forgetting in DNNs

Paper content:
We present a large-scale empirical study of catastrophic forgetting (CF) in modern Deep Neural Network (DNN) models that perform sequential (or: incremental) learning.
A new experimental protocol is proposed that takes into account typical constraints encountered in application scenarios.
As the investigation is empirical, we evaluate CF behavior on the hitherto largest number of visual classification datasets, from each of which we construct a representative number of Sequential Learning Tasks (SLTs) in close alignment to previous works on CF.
Our results clearly indicate that there is no model that avoids CF for all investigated datasets and SLTs under application conditions.
We conclude with a discussion of potential solutions and workarounds to CF, notably for the EWC and IMM models.

assistant: text: We check DNN models for catastrophic forgetting using a new evaluation scheme that reflects typical application conditions, with surprising results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Functional Characterization of Randomly Initialized Gradient Descent in Deep ReLU Networks

Paper content:
Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems.
Using a functional view of these networks gives us a useful new lens with which to understand them.
This allows us us to theoretically or experimentally probe properties of these networks, including the effect of standard initializations, the value of depth, the underlying loss surface, and the origins of generalization.
One key result is that generalization results from smoothness of the functional approximation, combined with a flat initial approximation.
This smoothness increases with number of units, explaining why massively overparamaterized networks continue to generalize well.

assistant: text: A functional approach reveals that flat initialization, preserved by gradient descent, leads to generalization ability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: We're Here to Help: Crisis Communication and User Perception of Data Breaches

Paper content:
Data breaches involve information being accessed by unauthorized parties.
Our research concerns user perception of data breaches, especially issues relating to accountability.
A preliminary study indicated many people had weak understanding of the issues, and felt they themselves were somehow responsible.
We speculated that this impression might stem from organizational communication strategies.
We therefore compared texts from organizations with external sources, such as the news media.
This suggested that organizations use well-known crisis communication methods to reduce their reputational damage, and that these strategies align with repositioning of the narrative elements involved in the story.
We then conducted a quantitative study, asking participants to rate either organizational texts or news texts about breaches.
The findings of this study were in line with our document analysis, and suggest that organizational communication affects the users' perception of victimization, attitudes in data protection, and accountability.
Our study suggests some software design and legal implications supporting users to protect themselves and develop better mental models of security breaches.

assistant: text: "In this paper, we tested communication strategies' influence on users mental models of a data breach."
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Human-Understandable Explanations of Infeasibility for Resource-Constrained Scheduling Problems

Paper content:
Significant work has been dedicated to developing methods for communicating reasons for decision-making within au-
tomated scheduling systems to human users.
However, much less focus has been placed on communicating reasons for why
scheduling systems are unable to arrive at a feasible solution when over-constrained.
We investigate this problem in the
context of task scheduling.
We introduce the agent resource-constrained project scheduling problem (ARCPSP), an ex-
tension of the resource-constrained project scheduling problem which includes a conception of agents that execute tasks
in parallel.
We outline a generic framework, based on efficiently enumerating minimal unsatisfiable sets (MUS) and
maximal satisfiable sets (MSS), to produce small descriptions of the source of infeasibility.
These descriptions are supple-
mented with potential relaxations that would fix the infeasibility found within the problem instance.
We illustrate how
this method may be applied to the ARCPSP and demonstrate how to generate different types of explanations for an over-
constrained instance of the ARCPSP.

assistant: text: We develop a framework for generating human-understandable explanations for why infeasibility is occurring in over-constrained instances of a class of resource-constrained scheduling problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning undirected models via query training

Paper content:
Typical amortized inference in variational autoencoders is specialized for a single probabilistic query.
Here we propose an inference network architecture that generalizes to unseen probabilistic queries.
Instead of an encoder-decoder pair, we can train a single inference network directly from data, using a cost function that is stochastic not only over samples, but also over queries.
We can use this network to perform the same inference tasks as we would in an undirected graphical model with hidden variables, without having to deal with the intractable partition function.
The results can be mapped to the learning of an actual undirected model, which is a notoriously hard problem.
Our network also marginalizes nuisance variables as required.
We show that our approach generalizes to unseen probabilistic queries on also unseen test data, providing fast and flexible inference.
Experiments show that this approach outperforms or matches PCD and AdVIL on 9 benchmark datasets.

assistant: text: Instead of learning the parameters of a graphical model from data, learn an inference network that can answer the same probabilistic queries.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Wasserstein Robust Reinforcement Learning

Paper content:
Reinforcement learning algorithms, though successful, tend to over-fit to training environments, thereby hampering their application to the real-world.
This paper proposes $\text{W}\text{R}^{2}\text{L}$ -- a robust reinforcement learning algorithm with significant robust performance on low and high-dimensional control tasks.
Our method formalises robust reinforcement learning as a novel min-max game with a Wasserstein constraint for a correct and convergent solver.
Apart from the formulation, we also propose an efficient and scalable solver following a novel zero-order optimisation method that we believe can be useful to numerical optimisation in general.
We empirically demonstrate significant gains compared to standard and robust state-of-the-art algorithms on high-dimensional MuJuCo environments

assistant: text: An RL algorithm that learns to be robust to changes in dynamics
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bridging HMMs and RNNs through Architectural Transformations

Paper content:
A distinct commonality between HMMs and RNNs is that they both learn hidden representations for sequential data.
In addition, it has been noted that the backward computation  of  the  Baum-Welch  algorithm  for  HMMs  is  a  special  case  of  the back propagation algorithm used for neural networks (Eisner (2016)).
Do these observations  suggest  that,  despite  their  many apparent  differences,  HMMs  are a special case of RNNs?
In this paper,  we investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization, to answer this question.
In particular, we investigate three key design factors—independence assumptions between the hidden states and the observation, the placement of softmax, and the use of non-linearity—in order to pin down their empirical effects.
We present a comprehensive empirical study to provide insights on the interplay between expressivity and interpretability with respect to language modeling and parts-of-speech induction.

assistant: text: Are HMMs a special case of RNNs? We investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization and provide new insights.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Variational Sparse Topic Model

Paper content:
Effectively inferring discriminative and coherent latent topics of short texts is a critical task for many real world applications.
Nevertheless, the task has been proven to be a great challenge for traditional topic models due to the data sparsity problem induced by the characteristics of short texts.
Moreover, the complex inference algorithm also become a bottleneck for these traditional models to rapidly explore variations.
In this paper, we propose a novel model called Neural Variational Sparse Topic Model (NVSTM) based on a sparsity-enhanced topic model named Sparse Topical Coding (STC).
In the model, the auxiliary word embeddings are utilized to improve the generation of representations.
The Variational Autoencoder (VAE) approach is applied to inference the model efficiently, which makes the model easy to explore extensions for its black-box inference process.
Experimental results onWeb Snippets, 20Newsgroups, BBC and Biomedical datasets show the effectiveness and efficiency of the model.

assistant: text: a neural sparsity-enhanced topic model based on VAE
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks

Paper content:
There exists a plethora of techniques for inducing structured sparsity in parametric models during the optimization process, with the final goal of resource-efficient inference.
However, to the best of our knowledge, none target a specific number of floating-point operations (FLOPs) as part of a single end-to-end optimization objective, despite reporting FLOPs as part of the results.
Furthermore, a one-size-fits-all approach ignores realistic system constraints, which differ significantly between, say, a GPU and a mobile phone -- FLOPs on the former incur less latency than on the latter; thus, it is important for practitioners to be able to specify a target number of FLOPs during model compression.
In this work, we extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective and show that, given a desired FLOPs requirement, different neural networks can be successfully trained for image classification.

assistant: text: We extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective, and we show that, given a desired FLOPs requirement, different neural networks are successfully trained.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Arbitrarily-conditioned Data Imputation

Paper content:
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows.
The proposed model is capable of mapping any partial data to a multi-modal latent variational distribution.
Sampling from such a distribution leads to stochastic imputation.
Preliminary evaluation on MNIST dataset shows promising stochastic imputation conditioned on partial images as input.

assistant: text: We propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Harnessing Structures for Value-Based Planning and Reinforcement Learning

Paper content:
Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL).
In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL.
In particular, if the underlying system dynamics lead to some global structures of the Q function, one should be capable of inferring the function better by leveraging such structures.
Specifically, we investigate the low-rank structure, which widely exists for big data matrices.
We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks (Atari games).
As our key contribution, by leveraging Matrix Estimation (ME) techniques, we propose a general framework to exploit the underlying low-rank structure in Q functions, leading to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on ''low-rank'' tasks.
Extensive experiments on control tasks and Atari games confirm the efficacy of our approach.

assistant: text: We propose a generic framework that allows for exploiting the low-rank structure in both planning and deep reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to diagnose from scratch by exploiting dependencies among labels

Paper content:
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.
Many tasks in radiology, for example, are largely problems of multi-label classification wherein medical images are interpreted to indicate multiple present or suspected pathologies.
Clinical settings drive the necessity for high accuracy simultaneously across a multitude of pathological outcomes and greatly limit the utility of tools which consider only a subset.
This issue is exacerbated by a general scarcity of training data and maximizes the need to extract clinically relevant features from available samples -- ideally without the use of pre-trained models which may carry forward undesirable biases from tangentially related tasks.
We present and evaluate a partial solution to these constraints in using LSTMs to leverage interdependencies among target labels in predicting 14 pathologic patterns from chest x-rays and establish state of the art results on the largest publicly available chest x-ray dataset from the NIH without pre-training.
Furthermore, we propose and discuss alternative evaluation metrics and their relevance in clinical practice.

assistant: text: we present the state-of-the-art results of using neural networks to diagnose chest x-rays
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reinforcement and Imitation Learning for Diverse Visuomotor Skills

Paper content:
We propose a general deep reinforcement learning method and apply it to robot manipulation tasks.
Our approach leverages demonstration data to assist a reinforcement learning agent in learning to solve a wide range of tasks, mainly previously unsolved.
We train visuomotor policies end-to-end to learn a direct mapping from RGB camera inputs to joint velocities.
Our experiments indicate that our reinforcement and imitation approach can solve contact-rich robot manipulation tasks that neither the state-of-the-art reinforcement nor imitation learning method can solve alone.
We also illustrate that these policies achieved zero-shot sim2real transfer by training with large visual and dynamics variations.

assistant: text: combine reinforcement learning and imitation learning to solve complex robot manipulation tasks from pixels
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Critical Percolation as a Framework to Analyze the Training of Deep Networks

Paper content:
In this paper we approach two relevant deep learning topics:
i) tackling of graph structured input data and
ii) a better understanding and analysis of deep networks and related learning algorithms.
With this in mind we focus on the topological classification of reachability in a particular subset of planar graphs (Mazes).
Doing so, we are able to model the topology of data while staying in Euclidean space, thus allowing its processing with standard CNN architectures.
We suggest a suitable architecture for this problem and show that it can express a perfect solution to the classification task.
The shape of the cost function around this solution is also derived and, remarkably, does not depend on the size of the maze in the large maze limit.
Responsible for this behavior are rare events in the dataset which strongly regulate the shape of the cost function near this global minimum.
We further identify an obstacle to learning in the form of poorly performing local minima in which the network chooses to ignore some of the inputs.
We further support our claims with training experiments and numerical analysis of the cost function on networks with up to $128$ layers.

assistant: text: A toy dataset based on critical percolation in a planar graph provides an analytical window to the training dynamics of deep neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisiting Fine-tuning for Few-shot Learning

Paper content:
Few-shot learning is the process of learning novel classes using only a few examples and it remains a challenging task in machine learning.
Many sophisticated few-shot learning algorithms have been proposed based on the notion that networks can easily overfit to novel examples if they are simply fine-tuned using only a few examples.
In this study, we show that in the commonly used low-resolution mini-ImageNet dataset, the fine-tuning method achieves higher accuracy than common few-shot learning algorithms in the 1-shot task and nearly the same accuracy as that of the state-of-the-art algorithm in the 5-shot task.
We then evaluate our method with more practical tasks, namely the high-resolution single-domain and cross-domain tasks.
With both tasks, we show that our method achieves higher accuracy than common few-shot learning algorithms.
We further analyze the experimental results and show that:
1) the retraining process can be stabilized by employing a low learning rate,
2) using adaptive gradient optimizers during fine-tuning can increase test accuracy, and
3) test accuracy can be improved by updating the entire network when a large domain-shift exists between base and novel classes.

assistant: text: An empirical study that provides a novel perspective on few-shot learning, in which a fine-tuning method shows comparable accuracy to more complex state-of-the-art methods in several classification tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Simple Fully Connected Network for Composing Word Embeddings from Characters

Paper content:
This work introduces a simple network for producing character aware word embeddings.
Position agnostic and position aware character embeddings are combined to produce an embedding vector for each word.
The learned word representations are shown to be very sparse and facilitate improved results on language modeling tasks, despite using markedly fewer parameters, and without the need to apply dropout.
A final experiment suggests that weight sharing contributes to sparsity, increases performance, and prevents overfitting.

assistant: text: A fully connected architecture is used to produce word embeddings from character representations, outperforms traditional embeddings and provides insight into sparsity and dropout.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Detecting Extrapolation with Local Ensembles

Paper content:
We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model.
We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class.
Our method uses local second-order information to approximate the variance of predictions across an ensemble of models from the same class.
We compute this approximation by estimating the norm of the component of a test point's gradient that aligns with the low-curvature directions of the Hessian, and provide a tractable method for estimating this quantity.
Experimentally, we show that our method is capable of detecting when a pre-trained model is extrapolating on test data, with applications to out-of-distribution detection, detecting spurious correlates, and active learning.

assistant: text: We present local ensembles, a method for detecting extrapolation in trained models, which approximates the variance of an ensemble using local-second order information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diminishing Batch Normalization

Paper content:
In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way.
Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice.
Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters.
We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters.
Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant.
To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced.
We analyze a two-layer model with arbitrary activation function.
The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not.
The convergence analysis applies to any activation function that satisfies our common assumptions.
For the analysis, we also show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence.
In the numerical experiments, we use more complex models with more layers and ReLU activation.
We observe that DBN outperforms the original BN algorithm on Imagenet, MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.

assistant: text: We propose a extension of the batch normalization, show a first-of-its-kind convergence analysis for this extension and show in numerical experiments that it has better performance than the original batch normalizatin.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks

Paper content:
The interplay between inter-neuronal network topology and cognition has been studied deeply by connectomics researchers and network scientists, which is crucial towards understanding the remarkable efficacy of biological neural networks.
Curiously, the deep learning revolution that revived neural networks has not paid much attention to topological aspects.
The architectures of deep neural networks (DNNs) do not resemble their biological counterparts in the topological sense.
We bridge this gap by presenting initial results of Deep Connectomics Networks (DCNs) as DNNs with topologies inspired by real-world neuronal networks.
We show high classification accuracy obtained by DCNs whose architecture was inspired by the biological neuronal networks of C. Elegans and the mouse visual cortex.
Recent advancements in neural network models have emerged through research in network architectures (He et al., 2016; Krizhevsky et al., 2012; Simonyan & Zisserman, 2014) , optimization (Kingma & Ba, 2014; Liu et al., 2019; Luo et al., 2019) , and generalization techniques (Srivastava et al., 2014; Ioffe & Szegedy, 2015; Zhang et al., 2019) , with convolutional layers inspired from receptive fields and functional architecture in cats' visual cortex.
However, the field of deep neural networks, with all its neuro-biologically inspired building blocks, has mostly left the topology story out.
1 Curiously, in the Cambrian explosion of neural network architectures in the post-AlexNet era, none seem to be inspired by the ideas prevalent in the domain of brain connectomics.
We demonstrated initial findings from applying networks inspired by real-world neuronal network topologies to deep learning.
Our experiments show the trainability of a DNN based on the neuronal network C.Elegans and the visual cortex of a mouse with and without freezing the parameters of the graph modules, which outperforms WS graphs with good theoretical small-world properties.
In future work, we will examine more principled methods for constructing a DAG from these networks and examine the impact of spectral properties of the graph topologies used both in the architectures we proposed and in the architecture proposed by (Xie et al., 2019) , while extending to other connectomes.

assistant: text: Initial findings in the intersection of network neuroscience and deep learning. C. Elegans and a mouse visual cortex learn to recognize handwritten digits.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantum Semi-Supervised Kernel Learning

Paper content:
Quantum machine learning methods have the potential to facilitate learning using extremely large datasets.
While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.
One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors.
Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines.
The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.

assistant: text: We extend quantum SVMs to semi-supervised setting, to deal with the likely problem of many missing class labels in huge datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

Paper content:
Neural networks for structured data like graphs have been studied extensively in recent years.
To date, the bulk of research activity has focused mainly on static graphs.
However, most real-world networks are dynamic since their topology tends to change over time.
Predicting the evolution of dynamic graphs is a task of high significance in the area of graph mining.
Despite its practical importance, the task has not been explored in depth so far, mainly due to its challenging nature.
In this paper, we propose a model that predicts the evolution of dynamic graphs.
Specifically, we use a graph neural network along with a recurrent architecture to capture the temporal evolution patterns of dynamic graphs.
Then, we employ a generative model which predicts the topology of the graph at the next time step and constructs a graph instance that corresponds to that topology.
We evaluate the proposed model on several artificial datasets following common network evolving dynamics, as well as on real-world datasets.
Results demonstrate the effectiveness of the proposed model.

assistant: text: Combining graph neural networks and the RNN graph generative model, we propose a novel architecture that is able to learn from a sequence of evolving graphs and predict the graph topology evolution for the future timesteps
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).
The process of implementing client-side software based on a Graphical User Interface (GUI) mockup created by a designer is the responsibility of developers.
Implementing GUI code is, however, time-consuming and prevent developers from dedicating the majority of their time implementing the actual functionality and logic of the software they are building.
Moreover, the computer languages used to implement such GUIs are specific to each target runtime system; thus resulting in tedious and repetitive work when the software being built is expected to run on multiple platforms using native technologies.
In this paper, we describe a model trained end-to-end with stochastic gradient descent to simultaneously learns to model sequences and spatio-temporal visual features to generate variable-length strings of tokens from a single GUI image as input.Our first contribution is pix2code, a novel application of Convolutional and Recurrent Neural Networks to generate computer tokens from a single GUI screenshot as input.
That is, no engineered feature extraction pipeline nor expert heuristics was designed to process the input data; our model learns from the pixel values of the input image alone.
Our experiments demonstrate the effectiveness of our method for generating computer code for various platforms (i.e. iOS and Android native mobile interfaces, and multi-platform web-based HTML/CSS interfaces) without the need for any change or specific tuning to the model.
In fact, pix2code can be used as such to support different target languages simply by being trained on a different dataset.
A video demonstrating our system is available online 1 .Our
second contribution is the release of our synthesized datasets consisting of both GUI screenshots and associated source code for three different platforms. Our
datasets and our pix2code implemention are publicly available 2 to foster future research.

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: Learning from Samples of Variable Quality

Paper content:
Training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing.
This creates a fundamental quality-versus-quantity trade-off in the learning process.
Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data?
We argue that if the learner could somehow know and take the label-quality into account, we could get the best of both worlds.
To this end, we introduce “fidelity-weighted learning” (FWL), a semi-supervised student-teacher approach for training deep neural networks using weakly-labeled data.
FWL modulates the parameter updates to a student network, trained on the task we care about on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher, who has access to limited samples with high-quality labels.

assistant: text: We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

Paper content:
Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard.
In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution (100,000^2 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions.
The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models.
We propose a method for disease available during training.
Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge.
We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique fromatp the field of semantic segmentation and object detection.

assistant: text: We propose a weakly supervised learning method for the classification and localization of cancers in extremely high resolution histopathology whole slide images using only image-wide labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interpretable and robust blind image denoising with bias-free convolutional neural networks

Paper content:
Deep convolutional networks often append additive constant ("bias") terms to their convolution operations, enabling a richer repertoire of functional mappings.
Biases are also used to facilitate training, by subtracting mean response over batches of training images (a component of "batch normalization").
Recent state-of-the-art blind denoising methods seem to require these terms for their success.
Here, however, we show that bias terms used in most CNNs (additive constants, including those used for batch normalization) interfere with the interpretability of these networks, do not help performance, and in fact prevent generalization of performance to noise levels not including in the training data.
In particular, bias-free CNNs (BF-CNNs) are locally linear, and hence amenable to direct analysis with linear-algebraic tools.
These analyses provide interpretations of network functionality in terms of projection onto a union of low-dimensional subspaces, connecting the learning-based method to more traditional denoising methodology.
Additionally, BF-CNNs generalize robustly, achieving near-state-of-the-art performance at noise levels well beyond the range over which they have been trained.

assistant: text: We show that removing constant terms from CNN architectures provides interpretability of the denoising method via linear-algebra techniques and also boosts generalization performance across noise levels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Depth-Adaptive Transformer

Paper content:
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process.
In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate different ways to predict how much computation is required for a particular sequence.
Unlike dynamic computation in Universal Transformers, which applies the same set of layers iteratively, we apply different layers at every step to adjust both the amount of computation as well as the model capacity.
On IWSLT German-English translation our approach matches the accuracy of a well tuned baseline Transformer while using less than a quarter of the decoder layers.

assistant: text: Sequence model that dynamically adjusts the amount of computation for each input.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Testing Robustness Against Unforeseen Adversaries

Paper content:
Most existing defenses against adversarial attacks only consider robustness to L_p-bounded distortions.
In reality, the specific attack is rarely known in advance and adversaries are free to modify images in ways which lie outside any fixed distortion model; for example, adversarial rotations lie outside the set of L_p-bounded distortions.
In this work, we advocate measuring robustness against a much broader range of unforeseen attacks, attacks whose precise form is unknown during defense design.
We propose several new attacks and a methodology for evaluating a defense against a diverse range of unforeseen distortions.
First, we construct novel adversarial JPEG, Fog, Gabor, and Snow distortions to simulate more diverse adversaries.
We then introduce UAR, a summary metric that measures the robustness of a defense against a given distortion.
Using UAR to assess robustness against existing and novel attacks, we perform an extensive study of adversarial robustness.
We find that evaluation against existing L_p attacks yields redundant information which does not generalize to other attacks; we instead recommend evaluating against our significantly more diverse set of attacks.
We further find that adversarial training against either one or multiple distortions fails to confer robustness to attacks with other distortion types.
These results underscore the need to evaluate and study robustness against unforeseen distortions.

assistant: text: We propose several new attacks and a methodology to measure robustness against unforeseen adversarial attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Secret Revealer: Generative Model Inversion Attacks Against Deep Neural Networks

Paper content:
This paper studies \emph{model inversion attacks}, in which the access to a model is abused to infer information about the training data.
Since its first introduction by~\citet{fredrikson2014privacy}, such attacks have raised serious concerns given that training data usually contain sensitive information.
Thus far, successful model inversion attacks have only been demonstrated on simple models, such as linear regression and logistic regression.
Previous attempts to invert neural networks, even the ones with simple architectures, have failed to produce convincing results.
We present a novel attack method, termed the \emph{generative model inversion attack}, which can invert deep neural networks with high success rates.
Rather than reconstructing private training data from scratch, we leverage partial public information, which can be very generic, to learn a distributional prior via generative adversarial networks (GANs) and use it to guide the inversion process.
Moreover, we theoretically prove that a model's predictive power and its vulnerability to inversion attacks are indeed two sides of the same coin---highly predictive models are able to establish a strong correlation between features and labels, which coincides exactly with what an adversary exploits to mount the attacks.
Our experiments demonstrate that the proposed attack improves identification accuracy over the existing work by about $75\%$ for reconstructing face images from a state-of-the-art face recognition classifier.
We also show that differential privacy, in its canonical form, is of little avail to protect against our attacks.

assistant: text: We develop a privacy attack that can recover the sensitive input data of a deep net from its output
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Reverse-Engineering Black-Box Neural Networks

Paper content:
Many deployed learned models are black boxes: given input, returns output.
Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable.
This work shows that such attributes of neural networks can be exposed from a sequence of queries.
This has multiple implications.
On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model.
On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples.
Our paper suggests that it is actually hard to draw a line between white box and black box models.

assistant: text: Querying a black-box neural network reveals a lot of information about it; we propose novel "metamodels" for effectively extracting information from a black box.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Domain Adaptation Through Label Propagation: Learning Clustered and Aligned Features

Paper content:
The difficulty of obtaining sufficient labeled data for supervised learning has motivated domain adaptation, in which a classifier is trained in one domain, source domain, but operates in another, target domain.
Reducing domain discrepancy has improved the performance, but it is hampered by the embedded features that do not form clearly separable and aligned clusters.
We address this issue by propagating labels using a manifold structure, and by enforcing cycle consistency to align the clusters of features in each domain more closely.
Specifically, we prove that cycle consistency leads the embedded features distant from all but one clusters if the source domain is ideally clustered.
We additionally utilize more information from approximated local manifold and pursue local manifold consistency for more improvement.
Results for various domain adaptation scenarios show tighter clustering and an improvement in classification accuracy.

assistant: text: A novel domain adaptation method to align manifolds from source and target domains using label propagation for better accuracy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learn to Explain Efficiently via Neural Logic Inductive Learning

Paper content:
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.
In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP).
We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data.
In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster.
We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.

assistant: text: An efficient differentiable ILP model that learns first-order logic rules that can explain the data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Recover Sparse Signals

Paper content:
In compressed sensing, a primary problem to solve is to reconstruct a high dimensional sparse signal from a small number of observations.
In this work, we develop a new sparse signal recovery algorithm using reinforcement learning (RL) and Monte CarloTree Search (MCTS).
Similarly to orthogonal matching pursuit (OMP), our RL+MCTS algorithm chooses the support of the signal sequentially.
The key novelty is that the proposed algorithm learns how to choose the next support as opposed to following a pre-designed rule as in OMP.
Empirical results are provided to demonstrate the superior performance of the proposed RL+MCTS algorithm over existing sparse signal recovery algorithms.

assistant: text: Formulating sparse signal recovery as a sequential decision making problem, we develop a method based on RL and MCTS that learns a policy to discover the support of the sparse signal.