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

Title: On Global Feature Pooling for Fine-grained Visual Categorization

Paper content:
Global feature pooling is a modern variant of feature pooling providing better interpretatability and regularization.
Although alternative pooling methods exist (eg. max, lp norm, stochastic), the averaging operation is still the dominating global pooling scheme in popular models.
As fine-grained recognition requires learning subtle, discriminative features, we consider the question: is average pooling the optimal strategy?
We first ask: ``is there a difference between features learned by global average and max pooling?'' Visualization and quantitative analysis show that max pooling encourages learning features of different spatial scales.
We then ask ``is there a single global feature pooling variant that's most suitable for fine-grained recognition?'' A thorough evaluation of nine representative pooling algorithms finds that: max pooling outperforms average pooling consistently across models, datasets, and image resolutions; it does so by reducing the generalization gap; and generalized pooling's performance increases almost monotonically as it changes from average to max.
We finally ask: ``what's the best way to combine two heterogeneous pooling schemes?'' Common strategies struggle because of potential gradient conflict but the ``freeze-and-train'' trick works best.
We also find that post-global batch normalization helps with faster convergence and improves model performance consistently.

assistant: text: For fine-grained classification tasks, this paper validated that maxpooling would encourage sparser feature maps than and outperform avgpooling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Prediction of Multi-Agent Interactions from Partial Observations

Paper content:
We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.
Our method is based on a graph-structured variational recurrent neural network, which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states.
We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine.

assistant: text: The authors propose Graph VRNN which models the interaction of multiple agents by deploying a VRNN for each agent
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition

Paper content:
In this paper we study the problem of learning the weights of a deep convolutional neural network.
We consider a network where convolutions are carried out over non-overlapping patches with a single kernel in each layer.
We develop an algorithm for simultaneously learning all the kernels from the training data.
Our approach dubbed Deep Tensor Decomposition (DeepTD) is based on a rank-1 tensor decomposition.
We theoretically investigate DeepTD under a realizable model for the training data where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted convolutional kernels.
We show that DeepTD is data-efficient and provably works as soon as the sample size exceeds the total number of convolutional weights in the network.
Our numerical experiments demonstrate the effectiveness of DeepTD and verify our theoretical findings.

assistant: text: Analyzes the problem of learning a very special class of CNNs: each layers consists of a single filter, applied to non-overlapping patches of the input.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mitigating Posterior Collapse in Strongly Conditioned Variational Autoencoders

Paper content:
Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse.
We find, and empirically show, that one of the major reasons behind posterior collapse is rooted in the way that generative models are conditioned, i.e., through concatenation of the latent variable and the condition
. To mitigate this problem, we propose to explicitly make the latent variables depend on the condition by unifying the conditioning and latent variable sampling, thus coupling them so as to prevent the model from discarding the root of variations
. To achieve this, we develop a conditional Variational Autoencoder architecture that learns a distribution not only of the latent variables, but also of the condition, the latter acting as prior on the former
. Our experiments on the challenging tasks of conditional human motion prediction and image captioning demonstrate the effectiveness of our approach at avoiding posterior collapse
. Video results of our approach are anonymously provided in http://bit.ly/iclr2020

assistant: text: This paper considers strongly conditioned generative models, and proposes an objective function and a parameterisation of the variational distribution such that latent variables explicitly depend on input conditions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEAP: Learning Embeddings for Adaptive Pace

Paper content:
Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.
However, choosing a non-trivial scheduling method may drastically improve convergence.
In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).
Our method parameterizes mini-batches dynamically based on the \textit{easiness} and \textit{true diverseness} of the sample within a salient feature representation space.
In LEAP, we train an \textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.
The \textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \textit{easiness} from cross-entropy losses and \textit{true diverseness} of examples from the representation space sculpted by the \textit{embedding} CNN.
We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.
We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.

assistant: text: Experiments the classification accuracy on MNIST, FashionMNIST, and CIFAR-10 datasets to learn a representation with curriculum learning style minibatch selection in an end-to-end framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Gradient Push for Distributed Deep Learning

Paper content:
Large mini-batch parallel SGD is commonly used for distributed training of deep networks.
Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication.
In this work we show the applicability of Stochastic Gradient Push (SGP) for distributed training.
SGP uses a gossip algorithm called PushSum for approximate distributed averaging, allowing for much more loosely coupled communications which can be beneficial in high-latency or high-variability scenarios.
The tradeoff is that approximate distributed averaging injects additional noise in the gradient which can affect the train and test accuracies.
We prove that SGP converges to a stationary point of smooth, non-convex objective functions.
Furthermore, we validate empirically the potential of SGP.
For example, using 32 nodes with 8 GPUs per node to train ResNet-50 on ImageNet, where nodes communicate over 10Gbps Ethernet, SGP completes 90 epochs in around 1.5 hours while AllReduce SGD takes over 5 hours, and the top-1 validation accuracy of SGP remains within 1.2% of that obtained using AllReduce SGD.

assistant: text: For distributed training over high-latency networks, use gossip-based approximate distributed averaging instead of exact distribute averaging like AllReduce.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph Spectral Regularization For Neural Network Interpretability

Paper content:
Deep neural networks can learn meaningful representations of data.
However, these representations are hard to interpret.
For example, visualizing a latent layer is generally only possible for at most three dimensions.
Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer.
Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions.
To do so, we propose a class of regularizations we call \textit{Graph Spectral Regularizations} that impose graph-structure on latent layers.
This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters.
This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data.
First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.
Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets.
Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation.
In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations.
Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.

assistant: text: Imposing graph structure on neural network layers for improved visual interpretability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Disentangling Style and Content in Anime Illustrations

Paper content:
Existing methods for AI-generated artworks still struggle with generating high-quality stylized content, where high-level semantics are preserved, or separating fine-grained styles from various artists.
We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of variations when only one of them is labelled in general, and fully decompose complex anime illustrations into style and content in particular.
Training such model is challenging, since given a style, various content data may exist but not the other way round.
Our approach is divided into two stages, one that encodes an input image into a style independent content, and one based on a dual-conditional generator.
We demonstrate the ability to generate high-fidelity anime portraits with a fixed content and a large variety of styles from over a thousand artists, and vice versa, using a single end-to-end network and with applications in style transfer.
We show this unique capability as well as superior output to the current state-of-the-art.

assistant: text: Proposal for a method to learn disentangled style (artist) and content representations in anime.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEAP: Learning Embeddings for Adaptive Pace

Paper content:
Determining the optimal order in which data examples are presented to Deep Neural Networks during training is a non-trivial problem.
However, choosing a non-trivial scheduling method may drastically improve convergence.
In this paper, we propose a Self-Paced Learning (SPL)-fused Deep Metric Learning (DML) framework, which we call Learning Embeddings for Adaptive Pace (LEAP).
Our method parameterizes mini-batches dynamically based on the \textit{easiness} and \textit{true diverseness} of the sample within a salient feature representation space.
In LEAP, we train an \textit{embedding} Convolutional Neural Network (CNN) to learn an expressive representation space by adaptive density discrimination using the Magnet Loss.
The \textit{student} CNN classifier dynamically selects samples to form a mini-batch based on the \textit{easiness} from cross-entropy losses and \textit{true diverseness} of examples from the representation space sculpted by the \textit{embedding} CNN.
We evaluate LEAP using deep CNN architectures for the task of supervised image classification on MNIST, FashionMNIST, CIFAR-10, CIFAR-100, and SVHN.
We show that the LEAP framework converges faster with respect to the number of mini-batch updates required to achieve a comparable or better test performance on each of the datasets.

assistant: text: LEAP combines the strength of adaptive sampling with that of mini-batch online learning and adaptive representation learning to formulate a representative self-paced strategy in an end-to-end DNN training protocol.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Collapse of deep and narrow neural nets

Paper content:
Recent theoretical work has demonstrated that deep neural networks have superior performance over shallow networks, but their training is more difficult, e.g., they suffer from the vanishing gradient problem.
This problem can be typically resolved by the rectified linear unit (ReLU) activation.
However, here we show that even for such activation, deep and narrow neural networks (NNs) will converge to erroneous mean or median states of the target function depending on the loss with high probability.
Deep and narrow NNs are encountered in solving partial differential equations with high-order derivatives.
We demonstrate this collapse of such NNs both numerically and theoretically, and provide estimates of the probability of collapse.
We also construct a diagram of a safe region for designing NNs that avoid the collapse to erroneous states.
Finally, we examine different ways of initialization and normalization that may avoid the collapse problem.
Asymmetric initializations may reduce the probability of collapse but do not totally eliminate it.

assistant: text: Deep and narrow neural networks will converge to erroneous mean or median states of the target function depending on the loss with high probability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Can I trust you more? Model-Agnostic Hierarchical Explanations

Paper content:
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.
We propose Mahé, a novel approach to provide Model-Agnostic Hierarchical Explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances.
Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors.
Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully provides explanations of interactions that are context-free.

assistant: text: The authors extend the linear local attribution method LIME for interpreting black box models, and propose a method to discern between context-dependent and context-free interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ISA-VAE: Independent Subspace Analysis with Variational Autoencoders

Paper content:
Recent work has shown increased interest in using the Variational Autoencoder (VAE) framework to discover interpretable representations of data in an unsupervised way.
These methods have focussed largely on modifying the variational cost function to achieve this goal.
However, we show that methods like beta-VAE simplify the tendency of variational inference to underfit causing pathological over-pruning and over-orthogonalization of learned components.
In this paper we take a complementary approach: to modify the probabilistic model to encourage structured latent variable representations to be discovered.
Specifically, the standard VAE probabilistic model is unidentifiable: the likelihood of the parameters is invariant under rotations of the latent space.
This means there is no pressure to identify each true factor of variation with a latent variable.
We therefore employ a rich prior distribution, akin to the ICA model, that breaks the rotational symmetry.
Extensive quantitative and qualitative experiments demonstrate that the proposed prior mitigates the trade-off introduced by modified cost functions like beta-VAE and TCVAE between reconstruction loss and disentanglement.
The proposed prior allows to improve these approaches with respect to both disentanglement and reconstruction quality significantly over the state of the art.

assistant: text: A general framework to use the family of L^p-nested distributions as the prior for the code vector of VAE, demonstrating a higher MIG.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Device-agnostic Firmware Execution is Possible

Paper content:
With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems.
Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware.
In recent years, we have witnessed devastating security breaches targeting IoT devices.
These security concerns have significantly hamstrung further evolution of IoT technology.
In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices.
We do not encode into our emulator any specific information about a device.
Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly.
This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device.
To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator.
We successfully identified both self-injected and real-world vulnerabilities.

assistant: text: Device-agnostic Firmware Execution
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation

Paper content:
This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM).
Many standard GNN variants propagate information along the edges of a graph by computing ``messages'' based only on the representation of the source of each edge.
In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information.
Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods.
Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature.
Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.

assistant: text: new GNN formalism + extensive experiments; showing differences between GGNN/GCN/GAT are smaller than thought
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using effective dimension to analyze feature transformations in deep neural networks

Paper content:
In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.
In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations.
In particular, we use singular value decomposition to analyze the relevant variations in feature space.
These variations are formalized as the effective dimension of the embedding.
We consider how the effective dimension varies across layers within class.
We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation.
Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.

assistant: text: Neural networks that do a good job of classification project points into more spherical shapes before compressing them into fewer dimensions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reproducibility in Machine Learning for Health

Paper content:
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.
This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.
In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.
We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.
Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.

assistant: text: By analyzing more than 300 papers in recent machine learning conferences, we found that Machine Learning for Health (ML4H) applications lag behind other machine learning fields in terms of reproducibility metrics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Document Embeddings With CNNs

Paper content:
This paper proposes a new model for document embedding.
Existing approaches either require complex inference or use recurrent neural networks that are difficult to parallelize.
We take a different route and use recent advances in language modeling to develop a convolutional neural network embedding model.
This allows us to train deeper architectures that are fully parallelizable.
Stacking layers together increases the receptive filed allowing each successive layer to model increasingly longer range semantic dependences within the document.
Empirically we demonstrate superior results on two publicly available benchmarks.
Full code will be released with the final version of this paper.

assistant: text: This paper proposes using CNNs with a skip-gram like objective as a fast way to output document embeddings
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representing dynamically: An active process for describing sequential data

Paper content:
We propose an unsupervised method for building dynamic representations of sequential data, particularly of observed interactions.
The method simultaneously acquires representations of input data and its dynamics.
It is based on a hierarchical generative model composed of two levels.
In the first level, a model learns representations to generate observed data.
In the second level, representational states encode the dynamics of the lower one.
The model is designed as a Bayesian network with switching variables represented in the higher level, and which generates transition models.
The method actively explores the latent space guided by its knowledge and the uncertainty about it.
That is achieved by updating the latent variables from prediction error signals backpropagated to the latent space.
So, no encoder or inference models are used since the generators also serve as their inverse transformations.
The method is evaluated in two scenarios, with static images and with videos.
The results show that the adaptation over time leads to better performance than with similar architectures without temporal dependencies, e.g., variational autoencoders.
With videos, it is shown that the system extracts the dynamics of the data in states that highly correlate with the ground truth of the actions observed.

assistant: text: The paper proposes a Bayesian network model, realized as a neural network, that learns different data in the form of a linear dynamical system
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Structured Neural Summarization

Paper content:
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.
Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text.
In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

assistant: text: This work combines Graph Neural Networks with a sequential approach to abstractive summarization, effective across all datasets in comparison to external baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training

Paper content:
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs.
We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data.
We find that adversarial training increases the robustness of classifiers against shared perturbations.
Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations.
Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs.
However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring.
It thus makes successful attacks on the classifier in the physical world less likely.
Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.

assistant: text: We empirically show that adversarial training is effective for removing universal perturbations, makes adversarial examples less robust to image transformations, and leaves them detectable for a detection approach.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Optimization of Sorting Networks via Continuous Relaxations

Paper content:
Sorting input objects is an important step in many machine learning pipelines.
However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization.
In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting operator from permutation matrices to the set of unimodal row-stochastic matrices, where every row sums to one and has a distinct argmax.
This relaxation permits straight-through optimization of any computational graph involve a sorting operation.
Further, we use this relaxation to enable gradient-based stochastic optimization over the combinatorially large space of permutations by deriving a reparameterized gradient estimator for the Plackett-Luce family of distributions over permutations.
We demonstrate the usefulness of our framework on three tasks that require learning semantic orderings of high-dimensional objects, including a fully differentiable, parameterized extension of the k-nearest neighbors algorithm

assistant: text: We provide a continuous relaxation to the sorting operator, enabling end-to-end, gradient-based stochastic optimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Diffusing Policies : Towards Wasserstein Policy Gradient Flows

Paper content:
Policy gradients methods often achieve better performance when the change in policy is limited to a small Kullback-Leibler divergence.
We derive policy gradients where the change in policy is limited to a small Wasserstein distance (or trust region).
This is done in the discrete and continuous multi-armed bandit settings with entropy regularisation.
We show that in the small steps limit with respect to the Wasserstein distance $W_2$, policy dynamics are governed by the heat equation, following the Jordan-Kinderlehrer-Otto result.
This means that policies undergo diffusion and advection, concentrating near actions with high reward.
This helps elucidate the nature of convergence in the probability matching setup, and provides justification for empirical practices such as Gaussian policy priors and additive gradient noise.

assistant: text: Linking Wasserstein-trust region entropic policy gradients, and the heat equation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Semi-Supervised Anomaly Detection

Paper content:
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
Typically anomaly detection is treated as an unsupervised learning problem.
In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of labeled samples, e.g. a subset verified by some domain expert as being normal or anomalous.
Semi-supervised approaches to anomaly detection aim to utilize such labeled samples, but most proposed methods are limited to merely including labeled normal samples.
Only a few methods take advantage of labeled anomalies, with existing deep approaches being domain-specific.
In this work we present Deep SAD, an end-to-end deep methodology for general semi-supervised anomaly detection.
Using an information-theoretic perspective on anomaly detection, we derive a loss motivated by the idea that the entropy of the latent distribution for normal data should be lower than the entropy of the anomalous distribution.
We demonstrate in extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10, along with other anomaly detection benchmark datasets, that our method is on par or outperforms shallow, hybrid, and deep competitors, yielding appreciable performance improvements even when provided with only little labeled data.

assistant: text: A new method to find anomaly data, when some labeled anomalies are given, that applies information theory-derived loss based on normal data usuallly having lower entropy than abnormal data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evaluating the distribution learning capabilities of GANs

Paper content:
We evaluate the distribution learning capabilities of generative adversarial networks by testing them on synthetic datasets.
The datasets include common distributions of points in $R^n$ space and images containing polygons of various shapes and sizes.
We find that by and large GANs fail to faithfully recreate point datasets which contain discontinous support or sharp bends with noise.
Additionally, on image datasets, we find that GANs do not seem to learn to count the number of objects of the same kind in an image.
We also highlight the apparent tension between generalization and learning in GANs.

assistant: text: GANs are evaluated on synthetic datasets
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating Information Flow in DNNs

Paper content:
We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory.
The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases.
Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X).
This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works.
To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters.
This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations.
We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.
By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class.
Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space.
Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering.
This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

assistant: text: Deterministic deep neural networks do not discard information, but they do cluster their inputs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Soft Value Iteration Networks for Planetary Rover Path Planning

Paper content:
Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable.
In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers.
The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function.
In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).
SVIN is designed to produce more effective training gradients through the value iteration network.
It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action.
We demonstrate the effectiveness of the proposed method in robot motion planning scenarios.
In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the  Curiosity rover that is currently operating on Mars.

assistant: text: This paper learns a reward function based on expert trajectories using a Value Iteration Module to make the planning step differentiable
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Correlating neural and symbolic representations of language

Paper content:
Analysis methods which enable us to better understand the
  representations and functioning of neural models of language are
  increasingly needed as deep learning becomes the dominant approach
  in NLP.
Here we present two methods based on Representational
  Similarity Analysis (RSA) and Tree Kernels (TK) which allow us to
  directly quantify how strongly the information encoded in neural
  activation patterns corresponds to information represented by
  symbolic structures such as syntax trees.
We first validate our
  methods on the case of a simple synthetic language for arithmetic
  expressions with clearly defined syntax and semantics, and show that
  they exhibit the expected pattern of results.
We then apply our methods to
  correlate neural representations of English sentences with their
  constituency parse trees.

assistant: text: Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Classification as Decoder: Trading Flexibility for Control in Multi Domain Dialogue

Paper content:
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.
They can learn from large unlabeled conversation datasets, build a deep understanding of conversational context, and generate a wide variety of responses.
This flexibility comes at the cost of control.
Undesirable responses in the training data will be reproduced by the model at inference time, and longer generations often don’t make sense.
Instead of generating responses one word at a time, we train a classifier to choose from a predefined list of full responses.
The classifier is trained on (conversation context, response class) pairs, where each response class is a noisily labeled group of interchangeable responses.
At inference, we generate the exemplar response associated with the predicted response class.
Experts can edit and improve these exemplar responses over time without retraining the classifier or invalidating old training data.
Human evaluation of 775 unseen doctor/patient conversations shows that this tradeoff improves responses.
Only 12% of our discriminative approach’s responses are worse than the doctor’s response in the same conversational context, compared to 18% for the generative model.
A discriminative model trained without any manual labeling of response classes achieves equal performance to the generative model.

assistant: text: Avoid generating responses one word at a time by using weak supervision to training a classifier  to pick a full response.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

Paper content:
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.
In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation.
We introduce TimbreTron, a method for musical timbre transfer which applies “image” domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer.
We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance.
Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.
We made an accompanying demo video here: https://www.cs.toronto.edu/~huang/TimbreTron/index.html which we strongly encourage you to watch before reading the paper.

assistant: text: Describes a model for musical timbre transfer with the results indicating that the proposed system is effective for pitch and tempo transfer, as well as timbre adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature-Robustness, Flatness and Generalization Error for Deep Neural Networks

Paper content:
The performance of deep neural networks is often attributed to their automated, task-related feature construction.
It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples.
Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error.
For several flatness measures, this correlation has been empirically validated.
However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily.
This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization.
The proposed measures imply a robustness of the network to changes in the input and the hidden layers.
Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data.
With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.

assistant: text: This paper defines a notion of feature-robustness and combines it with epsilon representativeness of a function to describe a connection between flatness of minima and generalization in deep neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Regularization Matters in Policy Optimization

Paper content:
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention  thanks to its encouraging performance on a variety of control tasks.
Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment.
In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks.
Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvement on the task performance, and the improvement is typically more significant when the task is more difficult.
We also compare with the widely used entropy regularization and find $L_2$ regularization is generally better.
Our findings are further confirmed to be robust against the choice of training hyperparameters.
We also study the effects of regularizing different components and find that only regularizing the policy network is typically enough.
We hope our study provides guidance for future practices in regularizing policy optimization algorithms.

assistant: text: This paper provides a study on the effect of regularization on performance in training environments in policy optimization methods in multiple continuous control tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking Generalized Matrix Factorization for Recommendation: The Importance of Multi-hot Encoding

Paper content:
Learning good representations of users and items is crucially important to recommendation with implicit feedback.
Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix.
However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information.
In this paper, we propose a novel Augmented Generalized Matrix Factorization (AGMF) approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items.
Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts.
Furthermore, the ablation study demonstrates that by using multi-hot encoding to enrich user embedding and item embedding for Generalized Matrix Factorization, better performance, faster convergence, and lower training loss can be achieved.

assistant: text: A simple extension of generalized matrix factorization can outperform state-of-the-art approaches for recommendation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: From Inference to Generation: End-to-end Fully Self-supervised Generation of Human Face from Speech

Paper content:
This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.
To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.
First, the inference networks are trained to match the speaker identity between the two different modalities.
Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.

assistant: text: This paper proposes a method of end-to-end multi-modal generation of human face from speech based on a self-supervised learning framework.