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

Title: Provable Convergence and Global Optimality  of Generative Adversarial Network

Paper content:
Generative adversarial networks (GANs) train implicit generative models through solving minimax problems.
Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood.
In this paper, we consider GANs in the type of the integral probability metrics (IPMs) with the generator represented by an overparametrized neural network.
When the discriminator is solved to approximate optimality in each iteration, we prove that stochastic gradient descent on a regularized IPM objective converges globally to a stationary point with a sublinear rate.
Moreover, we prove that when the width of the generator network is sufficiently large and the discriminator function class has enough discriminative ability, the obtained stationary point corresponds to a generator that yields a distribution that is close to the distribution of the observed data in terms of the total variation.
To the best of our knowledge, we seem to first establish both the global convergence and global optimality of training GANs when the generator is parametrized by a neural network.

assistant: text: We establish global convergence to optimality for IPM-based GANs where the generator is an overparametrized neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware

Paper content:
With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization.
Despite plenty of prior work on the quantization of weights or activations for neural networks, there is still a wide gap between the software quantizers and the low-precision accelerator implementation, which degrades either the efficiency of networks or that of the hardware for the lack of software and hardware coordination at design-phase.
In this paper, we propose a learned linear symmetric quantizer for integer neural network processors, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators (e.g., 16-bit) and multipliers (e.g., 4-bit).
We use a unified way to quantize weights and activations, and the results outperform many previous approaches for various networks such as AlexNet, ResNet, and lightweight models like MobileNet while keeping friendly to the accelerator architecture.
Additional, we also apply the method to object detection models and witness high performance and accuracy in YOLO-v2.
Finally, we deploy the quantized models on our specialized integer-arithmetic-only DNN accelerator to show the effectiveness of the proposed quantizer.
We show that even with linear symmetric quantization, the results can be better than asymmetric or non-linear methods in 4-bit networks.
In evaluation, the proposed quantizer induces less than 0.4\% accuracy drop in ResNet18, ResNet34, and AlexNet when quantizing the whole network as required by the integer processors.

assistant: text: We introduce an efficient quantization process that allows for performance acceleration on specialized integer-only neural network accelerator.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Statistically Consistent Saliency Estimation

Paper content:
The use of deep learning for a wide range of data problems has increased the need for understanding and diagnosing these models, and deep learning interpretation techniques have become an essential tool for data analysts.
Although numerous model interpretation methods have been proposed in recent years, most of these procedures are based on heuristics with little or no theoretical guarantees.
In this work, we propose a statistical framework for saliency estimation for black box computer vision models.
We build a model-agnostic estimation procedure that is statistically consistent and passes the saliency checks of Adebayo et al. (2018).
Our method requires solving a linear program, whose solution can be efficiently computed in polynomial time.
Through our theoretical analysis, we establish an upper bound on the number of model evaluations needed to recover the region of importance with high probability, and build a new perturbation scheme for estimation of local gradients that is shown to be more efficient than the commonly used random perturbation schemes.
Validity of the new method is demonstrated through sensitivity analysis.

assistant: text: We propose a statistical framework and a theoretically consistent procedure for saliency estimation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Adversarial Grammars for Future Prediction

Paper content:
In this paper, we propose a differentiable adversarial grammar model for future prediction.
The objective is to model a formal grammar in terms of differentiable functions and latent representations, so that their learning is possible through standard backpropagation.
Learning a formal grammar represented with latent terminals, non-terminals, and productions rules allows capturing sequential structures with multiple possibilities from data.
The adversarial grammar is designed so that it can learn stochastic production rules from the data distribution.
Being able to select multiple production rules leads to different predicted outcomes, thus efficiently modeling many plausible futures.
We confirm the benefit of the adversarial grammar on two diverse tasks: future 3D human pose prediction and future activity prediction.
For all settings, the proposed adversarial grammar outperforms the state-of-the-art approaches, being able to predict much more accurately and further in the future, than prior work.

assistant: text: We design a grammar that is learned in an adversarial setting and apply it to future prediction in video.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications

Paper content:
Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices.
These chips are mainly used in computer vision applications.
However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text.
In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips.
Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW.
For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip.
At the workshop, we have two system demonstrations for NLP tasks.
The first demo classifies the input English Wikipedia sentence into one of the 14 classes.
The second demo classifies the Chinese online-shopping review into positive or negative.

assistant: text: Deploy text classification and sentiment analysis applications for English and Chinese on a 300mW CNN accelerator chip for on-device application scenarios.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Chopout: A Simple Way to Train Variable Sized Neural Networks at Once

Paper content:
Large deep neural networks require huge memory to run and their running speed is sometimes too slow for real applications.
Therefore network size reduction with keeping accuracy is crucial for practical applications.
We present a novel neural network operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible.
Chopout is easy to implement and integrate into most type of existing neural networks.
Furthermore it enables to reduce size of networks and latent representations even after training just by truncating layers.
We show its effectiveness through several experiments.

assistant: text: We present a novel simple operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

Paper content:
This paper we present a defogger, a model that learns to predict future hidden information from partial observations.
We formulate this model in the context of forward modeling and leverage spatial and sequential constraints and correlations via convolutional neural networks and long short-term memory networks, respectively.
We evaluate our approach on a large dataset of human games of StarCraft: Brood War, a real-time strategy video game.
Our models consistently beat strong rule-based baselines and qualitatively produce sensible future game states.

assistant: text: This paper presents a defogger, a model that learns to predict future hidden information from partial observations, applied to a StarCraft dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Garbage in, model out: Weight theft with just noise

Paper content:
This paper explores the scenarios under which
an attacker can claim that ‘Noise and access to
the softmax layer of the model is all you need’
to steal the weights of a convolutional neural network
whose architecture is already known.
We
were able to achieve 96% test accuracy using
the stolen MNIST model and 82% accuracy using
stolen KMNIST model learned using only
i.i.d. Bernoulli noise inputs.
We posit that this
theft-susceptibility of the weights is indicative
of the complexity of the dataset and propose a
new metric that captures the same.
The goal of
this dissemination is to not just showcase how far
knowing the architecture can take you in terms of
model stealing, but to also draw attention to this
rather idiosyncratic weight learnability aspects of
CNNs spurred by i.i.d. noise input.
We also disseminate
some initial results obtained with using
the Ising probability distribution in lieu of the i.i.d.
Bernoulli distribution

assistant: text: Input only noise , glean the softmax outputs, steal the weights
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Layers as Stochastic Solvers

Paper content:
We provide a novel perspective on the forward pass through a block of layers in a deep network.
In particular, we show that a forward pass through a standard dropout layer followed by a linear layer and a non-linear activation is equivalent to optimizing a convex objective with a single iteration of a $\tau$-nice Proximal Stochastic Gradient method.
We further show that replacing standard Bernoulli dropout with additive dropout is equivalent to optimizing the same convex objective with a variance-reduced proximal method.
By expressing both fully-connected and convolutional layers as special cases of a high-order tensor product, we unify the underlying convex optimization problem in the tensor setting and derive a formula for the Lipschitz constant $L$ used to determine the optimal step size of the above proximal methods.
We conduct experiments with standard convolutional networks applied to the CIFAR-10 and CIFAR-100 datasets and show that replacing a block of layers with multiple iterations of the corresponding solver, with step size set via $L$, consistently improves classification accuracy.

assistant: text: A framework that links deep network layers to stochastic optimization algorithms; can be used to improve model accuracy and inform network design.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Functional Extension of Multi-Output Learning

Paper content:
When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers.
We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.

assistant: text: We propose an extension of multi-output learning to a continuum of tasks using operator-valued kernels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Sequential Latent Variable Models with Autoregressive Flows

Paper content:
We propose an approach for sequence modeling based on autoregressive normalizing flows.
Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics.
This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques.
We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models.
Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.

assistant: text: We show how autoregressive flows can be used to improve sequential latent variable models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Significance of feedforward architectural differences between the ventral visual stream and DenseNet

Paper content:
There are many differences between convolutional networks and the ventral visual streams of primates.
For example, standard convolutional networks lack recurrent and lateral connections, cell dynamics, etc.
However, their feedforward architectures are somewhat similar to the ventral stream, and warrant a more detailed comparison.
A recent study found that the feedforward architecture of the visual cortex could be closely approximated as a convolutional network, but the resulting architecture differed from widely used deep networks in several ways.
The same study also found, somewhat surprisingly, that training the ventral stream of this network for object recognition resulted in poor performance.
This paper examines the performance of this network in more detail.
In particular, I made a number of changes to the ventral-stream-based architecture, to make it more like a DenseNet, and tested performance at each step.
I chose DenseNet because it has a high BrainScore, and because it has some cortex-like architectural features such as large in-degrees and long skip connections.
Most of the changes (which made the cortex-like network more like DenseNet) improved performance.
Further work is needed to better understand these results.
One possibility is that details of the ventral-stream architecture may be ill-suited to feedforward computation, simple processing units, and/or backpropagation, which could suggest differences between the way high-performance deep networks and the brain approach core object recognition.

assistant: text: An approximation of primate ventral stream as a convolutional network performs poorly on object recognition, and multiple architectural features contribute to this.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Supervised Community Detection with Line Graph Neural Networks

Paper content:
Community detection in graphs can be solved via spectral methods or posterior inference under certain probabilistic graphical models.
Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds in terms of the signal-to-noise ratio.
By recasting community detection as a node-wise classification problem on graphs, we can also study it from a learning perspective.
We present a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting.
We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multiclass stochastic block models, which is believed to reach the computational threshold in these cases.
In particular, we propose to augment GNNs with the non-backtracking operator defined on the line graph of edge adjacencies.
The GNNs are achieved good performance on real-world datasets.
In addition, we perform the first analysis of the optimization landscape of using (linear) GNNs to solve community detection problems, demonstrating that under certain simplifications and assumptions, the loss value at any local minimum is close to the loss value at the global minimum/minima.

assistant: text: We propose a novel graph neural network architecture based on the non-backtracking matrix defined over the edge adjacencies and demonstrate its effectiveness in community detection tasks on graphs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A NEW POINTWISE CONVOLUTION IN DEEP NEURAL NETWORKS THROUGH EXTREMELY FAST AND NON PARAMETRIC TRANSFORMS

Paper content:
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.
However, we found that these conventional transforms have the ability to capture the cross-channel correlations without any learnable parameters in DNNs.
This paper firstly proposes to apply conventional transforms on pointwise convolution, showing that such transforms significantly reduce the computational complexity of neural networks without accuracy performance degradation.
Especially for DWHT, it requires no floating point multiplications but only additions and subtractions, which can considerably reduce computation overheads.
In addition, its fast algorithm further reduces complexity of floating point addition from O(n^2) to O(nlog n).
These non-parametric and low computational properties construct extremely efficient networks in the number parameters and operations, enjoying accuracy gain.
Our proposed DWHT-based model gained 1.49% accuracy increase with 79.4% reduced parameters and 48.4% reduced FLOPs compared with its baseline model (MoblieNet-V1) on the CIFAR 100 dataset.

assistant: text: We introduce new pointwise convolution layers equipped with extremely fast conventional transforms in deep neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Emergence of Linguistic Communication from  Referential Games with Symbolic and Pixel Input

Paper content:
The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication tasks.
Here we scale up this research by using contemporary deep learning methods and by training reinforcement-learning neural network agents on referential communication games.
We extend previous work, in which agents were trained in symbolic environments, by developing agents which are able to learn from raw pixel data, a more challenging and realistic input representation.
We find that the degree of structure found in the input data affects the nature of the emerged protocols, and thereby corroborate the hypothesis that structured compositional language is most likely to emerge when agents perceive the world as being structured.

assistant: text: A controlled study of the role of environments with respect to properties in emergent communication protocols.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tangent-Normal Adversarial Regularization for Semi-supervised Learning

Paper content:
The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications.
In comparison to supervised learning, the key difficulty in semi-supervised learning is how to make full use of the unlabeled data.
In order to utilize manifold information provided by unlabeled data, we propose a novel regularization called the tangent-normal adversarial regularization, which is composed by two parts.
The two parts complement with each other and jointly enforce the smoothness along two different directions that are crucial for semi-supervised learning.
One is applied along the tangent space of the data manifold, aiming to enforce local invariance of the classifier on the manifold, while the other is performed on the normal space orthogonal to the tangent space, intending to impose robustness on the classifier against the noise causing the observed data deviating from the underlying data manifold.
Both of the two regularizers are achieved by the strategy of virtual adversarial training.
Our method has achieved state-of-the-art performance on semi-supervised learning tasks on both artificial dataset and practical datasets.

assistant: text: We propose a novel manifold regularization strategy based on adversarial training, which can significantly improve the performance of semi-supervised learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Adaptive Stress Testing Formulation

Paper content:
Validation is a key challenge in the search for safe autonomy.
Simulations are often either too simple to provide robust validation, or too complex to tractably compute.
Therefore, approximate validation methods are needed to tractably find failures without unsafe simplifications.
This paper presents the theory behind one such black-box approach: adaptive stress testing (AST).
We also provide three examples of validation problems formulated to work with AST.

assistant: text: A formulation for a black-box, reinforcement learning method to find the most-likely failure of a system acting in complex scenarios.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative model based on minimizing exact empirical Wasserstein distance

Paper content:
Generative Adversarial Networks (GANs) are a very powerful framework for generative modeling.
However, they are often hard to train, and learning of GANs often becomes unstable.
Wasserstein GAN (WGAN) is a promising framework to deal with the instability problem as it has a good convergence property.
One drawback of the WGAN is that it evaluates the Wasserstein distance in the dual domain, which requires some approximation, so that it may fail to optimize the true Wasserstein distance.
In this paper, we propose evaluating the exact empirical optimal transport cost efficiently in the primal domain and performing gradient descent with respect to its derivative to train the generator network.
Experiments on the MNIST dataset show that our method is significantly stable to converge, and achieves the lowest Wasserstein distance among the WGAN variants at the cost of some sharpness of generated images.
Experiments on the 8-Gaussian toy dataset show that better gradients for the generator are obtained in our method.
In addition, the proposed method enables more flexible generative modeling than WGAN.

assistant: text: We have proposed a flexible generative model that learns stably by directly minimizing exact empirical Wasserstein distance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semi-Supervised Named Entity Recognition with CRF-VAEs

Paper content:
We investigate methods for semi-supervised learning (SSL) of a neural linear-chain conditional random field (CRF) for Named Entity Recognition (NER) by treating the tagger as the amortized variational posterior in a generative model of text given tags.
We first illustrate how to incorporate a CRF in a VAE, enabling end-to-end training on semi-supervised data.
We then investigate a series of increasingly complex deep generative models of tokens given tags enabled by end-to-end optimization, comparing the proposed models against supervised and strong CRF SSL baselines on the Ontonotes5 NER dataset.
We find that our best proposed model consistently improves performance by $\approx 1\%$ F1 in low- and moderate-resource regimes and easily addresses degenerate model behavior in a more difficult, partially supervised setting.

assistant: text: We embed a CRF in a VAE of tokens and NER tags for semi-supervised learning and show improvements in low-resource settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling

Paper content:
For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.
VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator.
We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance.
This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion.
By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.

assistant: text: A novel Bayesian deep learning framework that captures and relates hierarchical semantic and visual concepts, performing well on a variety of image and text modeling and generation tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Newton Residual Learning

Paper content:
A plethora of computer vision tasks, such as optical flow and image alignment, can be formulated as non-linear optimization problems.
Before the resurgence of deep learning, the dominant family for solving such optimization problems was numerical optimization, e.g, Gauss-Newton (GN).
More recently, several attempts were made to formulate learnable GN steps as cascade regression architectures.
In this paper, we investigate recent machine learning architectures, such as deep neural networks with residual connections, under the above perspective.
To this end, we first demonstrate how residual blocks (when considered as discretization of ODEs) can be viewed as GN steps.
Then, we go a step further and propose a new residual block, that is reminiscent of Newton's method in numerical optimization and exhibits faster convergence.
We thoroughly evaluate the proposed Newton-ResNet by conducting experiments on image and speech classification and image generation, using 4 datasets.
All the experiments demonstrate that Newton-ResNet requires less parameters to achieve the same performance with the original ResNet.

assistant: text: We demonstrate how residual blocks can be viewed as Gauss-Newton steps; we propose a new residual block that exploits second order information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variance Reduction With Sparse Gradients

Paper content:
Variance reduction methods which use a mixture of large and small batch gradients, such as SVRG (Johnson & Zhang, 2013) and SpiderBoost (Wang et al., 2018), require significantly more computational resources per update than SGD (Robbins & Monro, 1951).
We reduce the computational cost per update of variance reduction methods by introducing a sparse gradient operator blending the top-K operator (Stich et al., 2018; Aji & Heafield, 2017) and the randomized coordinate descent operator.
While the computational cost of computing the derivative of a model parameter is constant, we make the observation that the gains in variance reduction are proportional to the magnitude of the derivative.
In this paper, we show that a sparse gradient based on the magnitude of past gradients reduces the computational cost of model updates without a significant loss in variance reduction.
Theoretically, our algorithm is at least as good as the best available algorithm (e.g. SpiderBoost) under appropriate settings of parameters and can be much more efficient if our algorithm succeeds in capturing the sparsity of the gradients.
Empirically, our algorithm consistently outperforms SpiderBoost using various models to solve various image classification tasks.
We also provide empirical evidence to support the intuition behind our algorithm via a simple gradient entropy computation, which serves to quantify gradient sparsity at every iteration.

assistant: text: We use sparsity to improve the computational complexity of variance reduction methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sample Efficient Imitation Learning for Continuous Control

Paper content:
The goal of imitation learning (IL) is to enable a learner to imitate expert behavior given expert demonstrations.
Recently, generative adversarial imitation learning (GAIL) has shown significant progress on IL for complex continuous tasks.
However, GAIL and its extensions require a large number of environment interactions during training.
In real-world environments, the more an IL method requires the learner to interact with the environment for better imitation, the more training time it requires, and the more damage it causes to the environments and the learner itself.
We believe that IL algorithms could be more applicable to real-world problems if the number of interactions could be reduced.
In this paper, we propose a model-free IL algorithm for continuous control.
Our algorithm is made up mainly three changes to the existing adversarial imitation learning (AIL) methods –
(a) adopting off-policy actor-critic (Off-PAC) algorithm to optimize the learner policy,
(b) estimating the state-action value using off-policy samples without learning reward functions, and
(c) representing the stochastic policy function so that its outputs are bounded.
Experimental results show that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions.

assistant: text: In this paper, we proposed a model-free, off-policy IL algorithm for continuous control. Experimental results showed that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Communication Algorithms via Deep Learning

Paper content:
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age.
Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century.
In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning.
We study a family of sequential codes parametrized by recurrent neural network (RNN) architectures.
We show that cre- atively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).
We show strong gen- eralizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.

assistant: text: We show that creatively designed and trained RNN architectures can decode well known sequential codes and achieve close to optimal performances.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Optimal Transport for Distribution Adaptation in Bayesian Hilbert Maps

Paper content:
Parameters are one of the most critical components of machine learning models.
As datasets and learning domains change, it is often necessary and time-consuming to re-learn entire models.
Rather than re-learning the parameters from scratch, replacing learning with optimization, we propose a framework building upon the theory of \emph{optimal transport} to adapt model parameters by discovering correspondences between models and data, significantly amortizing the training cost.
We demonstrate our idea on the challenging problem of creating probabilistic spatial representations for autonomous robots.
Although recent mapping techniques have facilitated robust occupancy mapping, learning all spatially-diverse parameters in such approximate Bayesian models demand considerable computational time, discouraging them to be used in real-world robotic mapping.
Considering the fact that the geometric features a robot would observe with its sensors are similar across various environments, in this paper, we demonstrate how to re-use parameters and hyperparameters learned in different domains.
This adaptation is computationally more efficient than variational inference and Monte Carlo techniques.
A series of experiments conducted on realistic settings verified the possibility of transferring thousands of such parameters with a negligible time and memory cost, enabling large-scale mapping in urban environments.

assistant: text: We present a method of adapting hyperparameters of probabilistic models using optimal transport with applications in robotics
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem

Paper content:
Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP).
A novel approach using variational inference for learning the reward function is proposed in this research.
Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action.
The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference.
This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent.
Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.

assistant: text: Using a supervised latent variable modeling framework to determine reward in inverse reinforcement learning task
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Benchmarking the Neural Linear Model for Regression

Paper content:
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning.
Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks.
In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced ''gap'' datasets, which are better tests of out-of-distribution uncertainty.
We demonstrate that the neural linear model is a simple method that shows competitive performance on these tasks.

assistant: text: We benchmark the neural linear model on the UCI and UCI "gap" datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The divergences minimized by non-saturating GANs

Paper content:
Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting extensions such as f-GANs and Wasserstein GANs.
For both classic GANs and f-GANs, there is an original variant of training and a "non-saturating" variant which uses an alternative form of generator update.
The original variant is theoretically easier to study, but the alternative variant frequently performs better and is recommended for use in practice.
The alternative generator update is often regarded as a simple modification to deal with optimization issues, and it appears to be a common misconception that the two variants minimize the same divergence.
In this short note we derive the divergences approximately minimized by the original and alternative variants of GAN and f-GAN training.
This highlights important differences between the two variants.
For example, we show that the alternative variant of KL-GAN training actually minimizes the reverse KL divergence, and that the alternative variant of conventional GAN training minimizes a "softened" version of the reverse KL.
We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.

assistant: text: Typical GAN training doesn't optimize Jensen-Shannon, but something like a reverse KL divergence.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Jiffy: A Convolutional Approach to Learning Time Series Similarity

Paper content:
Computing distances between examples is at the core of many learning algorithms for time series.
Consequently, a great deal of work has gone into designing effective time series distance measures.
We present Jiffy, a simple and scalable distance metric for multivariate time series.
Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective.
By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network.
Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.

assistant: text: Jiffy is a convolutional approach to learning a distance metric  for multivariate time series that outperforms existing methods in terms of nearest-neighbor classification accuracy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Lexical: A Semantic Retrieval Framework for Textual Search Engine

Paper content:
Search engine has become a fundamental component in various web and mobile applications.
Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries.
In this paper, we explore a vector space search framework for document retrieval.
Specifically, we trained a deep semantic matching model so that each query and document can be encoded as a low dimensional embedding.
Our model was trained based on BERT architecture.
We deployed a fast k-nearest-neighbor index service for online serving.
Both offline and online metrics demonstrate that our method improved retrieval performance and search quality considerably, particularly for tail queries

assistant: text: A deep semantic framework for textual search engine document retrieval
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model-based Saliency for the Detection of Adversarial Examples

Paper content:
Adversarial perturbations cause a shift in the salient features of an image, which may result in a misclassification.
We demonstrate that gradient-based saliency approaches are unable to capture this shift, and develop a new defense which detects adversarial examples based on learnt saliency models instead.
We study two approaches: a CNN trained to distinguish between natural and adversarial images using the saliency masks produced by our learnt saliency model, and a CNN trained on the salient pixels themselves as its input.
On MNIST, CIFAR-10 and ASSIRA, our defenses are able to detect various adversarial attacks, including strong attacks such as C&W and DeepFool, contrary to gradient-based saliency and detectors which rely on the input image.
The latter are unable to detect adversarial images when the L_2- and L_infinity- norms of the perturbations are too small.
Lastly, we find that the salient pixel based detector improves on saliency map based detectors as it is more robust to white-box attacks.

assistant: text: We show that gradients are unable to capture shifts in saliency due to adversarial perturbations and present an alternative adversarial defense using learnt saliency models that is effective against both black-box and white-box attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spectral Analysis of Kernel and Neural Embeddings:  Optimization and Generalization

Paper content:
We extend the recent results of (Arora et al., 2019) by a spectral analysis of representations corresponding to kernel and neural embeddings.
They showed that in a simple single layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines both the convergence of the optimization during training as well as the generalization properties.
We generalize their result to kernel and neural representations and show that these extensions improve both optimization and generalization of the basic setup studied in (Arora et al., 2019).

assistant: text: Spectral analysis for understanding how different representations can improve optimization and generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural TTS Stylization with Adversarial and Collaborative Games

Paper content:
The modeling of style when synthesizing natural human speech from text has been the focus of significant attention.
Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud.
The synthesized audio waveform is expected to contain the verbal content of x_txt and the auditory style of x_aud.
Unfortunately, modeling style in TTS is somewhat under-determined and training models with a reconstruction loss alone is insufficient to disentangle content and style from other factors of variation.
In this work, we introduce an end-to-end TTS model that offers enhanced content-style disentanglement ability and controllability.
We achieve this by combining a pairwise training procedure, an adversarial game, and a collaborative game into one training scheme.
The adversarial game concentrates the true data distribution, and the collaborative game minimizes the distance between real samples and generated samples in both the original space and the latent space.
As a result, the proposed model delivers a highly controllable generator, and a disentangled representation.
Benefiting from the separate modeling of style and content, our model can generate human fidelity speech that satisfies the desired style conditions.
Our model achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker's voice).

assistant: text: a generative adversarial network for style modeling in a text-to-speech system
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CAQL: Continuous Action Q-Learning

Paper content:
Reinforcement learning (RL) with value-based methods (e.g., Q-learning) has shown success in a variety of domains such as
games and recommender systems (RSs).
When the action space is finite, these algorithms implicitly finds a policy by learning the optimal value function, which are often very efficient.
However, one major challenge of extending Q-learning to tackle continuous-action RL problems is that obtaining optimal Bellman backup requires solving a continuous action-maximization (max-Q) problem.
While it is common to restrict the parameterization of the Q-function to be concave in actions to simplify the max-Q problem, such a restriction might lead to performance degradation.
Alternatively, when the Q-function is parameterized with a generic feed-forward neural network (NN), the max-Q problem can be NP-hard.
In this work, we propose the CAQL method which minimizes the Bellman residual using Q-learning with one of several plug-and-play action optimizers.
In particular, leveraging the strides of optimization theories in deep NN, we show that max-Q problem can be solved optimally with mixed-integer programming (MIP)---when the Q-function has sufficient representation power, this MIP-based optimization induces better policies and is more robust than counterparts, e.g., CEM or GA, that approximate the max-Q solution.
To speed up training of CAQL, we develop three techniques, namely
(i) dynamic tolerance,
(ii) dual filtering, and
(iii) clustering.
To speed up inference of CAQL, we introduce the action function that concurrently learns the optimal policy.
To demonstrate the efficiency of CAQL we compare it with state-of-the-art RL algorithms on benchmark continuous control problems that have different degrees of action constraints and show that CAQL significantly outperforms policy-based methods in heavily constrained environments.

assistant: text: A general framework of value-based reinforcement learning for continuous control
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Domain Adaptive Multibranch Networks

Paper content:
We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition.
To this end, we introduce a deep learning framework where each domain undergoes a different sequence of operations, allowing some, possibly more complex, domains to go through more computations than others.
This contrasts with  state-of-the-art domain adaptation techniques that force all domains to be processed with the same series of operations, even when using multi-stream architectures whose parameters are not shared.
As evidenced by our experiments, the greater flexibility of our method translates to higher accuracy.
Furthermore, it allows us to handle any number of domains simultaneously.

assistant: text: A Multiflow Network is a dynamic architecture for domain adaptation that learns potentially different computational graphs per domain, so as to map them to a common representation where inference can be performed in a domain-agnostic fashion.