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

Title: Behaviour Suite for Reinforcement Learning

Paper content:
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short.
bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives.
First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms.
Second, to study agent behaviour through their performance on these shared benchmarks.
To complement this effort, we open source this http URL, which automates evaluation and analysis of any agent on bsuite.
This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms.
Our code is Python, and easy to use within existing projects.
We include examples with OpenAI Baselines, Dopamine as well as new reference implementations.
Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.

assistant: text: Bsuite is a collection of carefully-designed experiments that investigate the core capabilities of RL agents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Solve Linear Inverse Problems in Imaging with Neumann Networks

Paper content:
Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.
Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem.
Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem.
We also investigate an extension of the Neumann network that incorporates a more sample efficient patch-based regularization approach.
This work explores the Neumann network architecture to solve linear inverse problems, which can be interpreted as an approximation of the MSE optimal reconstruction according to our Proposition 1.
The Neumann network architecture also permits a learned patchwise regularizer, which learns the low-dimensional conditional distributions over image patches instead of the whole image.
The Neumann network is empirically competitive with other state-of-the-art methods for inverse problems in imaging, and we demonstrate the ability to learn to regularize from a single training pair.

assistant: text: Neumann networks are an end-to-end, sample-efficient learning approach to solving linear inverse problems in imaging that are compatible with the MSE optimal approach and admit an extension to patch-based learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Target-directed Atomic Importance Estimation via Reverse Self-attention

Paper content:
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and material engineering.
The most common way to estimate the atomic importance is to compute the electronic structure using density-functional theory (DFT), and then to interpret it using domain knowledge of human experts.
However, this conventional approach is impractical to the large molecular database because DFT calculation requires huge computation, specifically, O(n^4) time complexity w.r.t. the number of electrons in a molecule.
Furthermore, the calculation results should be interpreted by the human experts to estimate the atomic importance in terms of the target molecular property.
To tackle this problem, we first exploit machine learning-based approach for the atomic importance estimation.
To this end, we propose reverse self-attention on graph neural networks and integrate it with graph-based molecular description.
Our method provides an efficiently-automated and target-directed way to estimate the atomic importance without any domain knowledge on chemistry and physics.

assistant: text: We first propose a fully-automated and target-directed atomic importance estimator based on the graph neural networks and a new concept of reverse self-attention.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: mixup: Beyond Empirical Risk Minimization

Paper content:
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples.
In this work, we propose mixup, a simple learning principle to alleviate these issues.
In essence, mixup trains a neural network on convex combinations of pairs of examples and their labels.
By doing so, mixup regularizes the neural network to favor simple linear behavior in-between training examples.
Our experiments on the ImageNet-2012, CIFAR-10, CIFAR-100, Google commands and UCI datasets show that mixup improves the generalization of state-of-the-art neural network architectures.
We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.

assistant: text: Training on convex combinations between random training examples and their labels improves generalization in deep neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using Clinical Notes for ICU Management

Paper content:
Monitoring patients in ICU is a challenging and high-cost task.
Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources.
There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments.
In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.
While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together.
We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.

assistant: text: We demostarte that using clinical notes in conjuntion with ICU instruments data improves the perfomance on ICU management benchmark tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: VARIATIONAL SGD: DROPOUT , GENERALIZATION AND CRITICAL POINT AT THE END OF CONVEXITY

Paper content:
The goal of the paper is to propose an algorithm for learning the most generalizable solution from given training data.
It is shown that Bayesian approach leads to a solution that dependent on statistics of training data and not on particular
samples.
The solution is stable under perturbations of training data because it is defined by an integral contribution of multiple maxima of the likelihood and not by a single global maximum.
Specifically, the Bayesian probability distribution
of parameters (weights) of a probabilistic model given by a neural network is estimated via recurrent variational approximations.
Derived recurrent update rules correspond to SGD-type rules for finding a minimum of an effective loss that is an average of an original negative log-likelihood over the Gaussian distributions of weights, which makes it a function of means and variances.
The effective loss is convex for large variances and non-convex in the limit of small variances.
Among stationary solutions of the update rules there are trivial solutions with zero variances at local minima of the original loss and a single non-trivial solution with finite variances that is a critical point at the end of convexity of the effective loss
in the mean-variance space.
At the critical point both first- and second-order gradients of the effective loss w.r.t. means are zero.
The empirical study confirms that the critical point represents the most generalizable solution.
While the location of
the critical point in the weight space depends on specifics of the used probabilistic model some properties at the critical point are universal and model independent.

assistant: text: Proposed method for finding the most generalizable solution that is stable w.r.t. perturbations of trainig data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Computation Reallocation for Object Detection

Paper content:
The allocation of computation resources in the backbone is a crucial issue in object detection.
However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal.
In order to reallocate the engaged computation resources in a more efficient way, we present CR-NAS (Computation Reallocation Neural Architecture Search) that can learn computation reallocation strategies across different feature resolution and spatial position diectly on the target detection dataset.
A two-level reallocation space is proposed for both stage and spatial reallocation.
A novel hierarchical search procedure is adopted to cope with the complex search space.
We apply CR-NAS to multiple backbones and achieve consistent improvements.
Our CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO AP respectively without any additional computation budget.
The models discovered by CR-NAS can be equiped to other powerful detection neck/head and be easily transferred to other dataset, e.g. PASCAL VOC, and other vision tasks, e.g. instance segmentation.
Our CR-NAS can be used as a plugin to improve the performance of various networks, which is demanding.

assistant: text: We propose CR-NAS to reallocate engaged  computation resources in different resolution and spatial position.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Preliminary Study of Disentanglement  With Insights on the Inadequacy of Metrics

Paper content:
Disentangled encoding is an important step towards a better representation learning.
However, despite the numerous efforts, there still is no clear winner that captures the independent features of the data in an unsupervised fashion.
In this work we empirically evaluate the performance of six unsupervised disentanglement approaches on the mpi3d toy dataset curated and released for the NeurIPS 2019 Disentanglement Challenge.
The methods investigated in this work are Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE.
The capacities of all models were progressively increased throughout the training and the hyper-parameters were kept intact across experiments.
The methods were evaluated based on five disentanglement metrics, namely, DCI, Factor-VAE, IRS, MIG, and SAP-Score.
Within the limitations of this study, the Beta-TCVAE approach was found to outperform its alternatives with respect to the normalized sum of metrics.
However, a qualitative study of the encoded latents reveal that there is not a consistent correlation between the reported metrics and the disentanglement potential of the model.

assistant: text: Inadequacy of Disentanglement Metrics
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning

Paper content:
In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability.
Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL).
This work shows that a differentiable activation function is not necessary any more for error backpropagation.
The derivative of the activation function can be replaced by an iterative temporal differencing (ITD) using fixed random feedback weight alignment (FBA).
Using FBA with ITD, we can transform the VBP into a more biologically plausible approach for learning deep neural network architectures.
We don't claim that ITD works completely the same as the spike-time dependent plasticity (STDP) in our brain but this work can be a step toward the integration of STDP-based error backpropagation in deep learning.

assistant: text: Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking the Hyperparameters for Fine-tuning

Paper content:
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks.
Current practices for fine-tuning typically involve selecting an ad-hoc choice of hyper-parameters and keeping them fixed to values normally used for training from scratch.
This paper re-examines several common practices of setting hyper-parameters for fine-tuning.
Our findings are based on extensive empirical evaluation for fine-tuning on various transfer learning benchmarks.
(1) While prior works have thoroughly investigated learning rate and batch size, momentum for fine-tuning is a relatively unexplored parameter.
We find that picking the right value for momentum is critical for fine-tuning performance and connect it with previous theoretical findings.
(2) Optimal hyper-parameters for fine-tuning in particular the effective learning rate are not only dataset dependent but also sensitive to the similarity between the source domain and target domain.
This is in contrast to hyper-parameters for training from scratch.
(3) Reference-based regularization that keeps models close to the initial model does not necessarily apply for "dissimilar" datasets.
Our findings challenge common practices of fine- tuning and encourages deep learning practitioners to rethink the hyper-parameters for fine-tuning.

assistant: text: This paper re-examines several common practices of setting hyper-parameters for fine-tuning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Supersymmetric Artificial Neural Network

Paper content:
The “Supersymmetric Artificial Neural Network” in deep learning (denoted (x; θ, bar{θ})Tw), espouses the importance of considering biological constraints in the aim of further generalizing backward propagation.
Looking at the progression of ‘solution geometries’; going from SO(n) representation (such as Perceptron like models) to SU(n) representation (such as UnitaryRNNs) has guaranteed richer and richer representations in weight space of the artificial neural network, and hence better and better hypotheses were generatable.
The Supersymmetric Artificial Neural Network explores a natural step forward, namely SU(m|n) representation.
These supersymmetric biological brain representations (Perez et al.) can be represented by supercharge compatible special unitary notation SU(m|n), or (x; θ, bar{θ})Tw parameterized by θ, bar{θ}, which are supersymmetric directions, unlike θ seen in the typical non-supersymmetric deep learning model.
Notably, Supersymmetric values can encode or represent more information than the typical deep learning model, in terms of “partner potential” signals for example.
Pertinently, the "Edward Witten/String theory powered supersymmetric artificial neural network", is one wherein supersymmetric weights are sought.Many machine learning algorithms are not empirically shown to be exactly biologically plausible, i.e. Deep Neural Network algorithms, have not been observed to occur in the brain, but regardless, such algorithms work in practice in machine learning.Likewise, regardless of Supersymmetry's elusiveness at the LHC, as seen above, it may be quite feasible to borrow formal methods from strategies in physics even if such strategies are yet to show related physical phenomena to exist; thus it may be pertinent/feasible to try to construct a model that learns supersymmetric weights, as I proposed throughout this paper, following the progression of solution geometries going from ( ) to ( ) and onwards to ( | ) BID15 .

assistant: text: Generalizing backward propagation, using formal methods from supersymmetry.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Integrative Tensor-based Anomaly Detection System For Satellites

Paper content:
Detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems.
Although there have been several studies in detecting anomalies based on rule-based or machine learning-based approaches for satellite systems, a tensor-based decomposition method has not been extensively explored for anomaly detection.
In this work, we introduce an Integrative Tensor-based Anomaly Detection (ITAD) framework to detect anomalies in a satellite system.
Because of the high risk and cost, detecting anomalies in a satellite system is crucial.
We construct 3rd-order tensors with telemetry data collected from Korea Multi-Purpose Satellite-2 (KOMPSAT-2) and calculate the anomaly score using one of the component matrices obtained by applying CANDECOMP/PARAFAC decomposition to detect anomalies.
Our result shows that our tensor-based approach can be effective in achieving higher accuracy and reducing false positives in detecting anomalies as compared to other existing approaches.

assistant: text: Integrative Tensor-based Anomaly Detection(ITAD) framework for a satellite system.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: D3PG: Deep Differentiable Deterministic Policy Gradients

Paper content:
Over the last decade, two competing control strategies have emerged for solving complex control tasks with high efficacy.
Model-based control algorithms, such as model-predictive control (MPC) and trajectory optimization, peer into the gradients of underlying system dynamics in order to solve control tasks with high sample efficiency.
However, like all gradient-based numerical optimization methods,model-based control methods are sensitive to intializations and are prone to becoming trapped in local minima.
Deep reinforcement learning (DRL), on the other hand, can somewhat alleviate these issues by exploring the solution space through sampling — at the expense of computational cost.
In this paper, we present a hybrid method that combines the best aspects of gradient-based methods and DRL.
We base our algorithm on the deep deterministic policy gradients (DDPG) algorithm and propose a simple modification that uses true gradients from a differentiable physical simulator to increase the convergence rate of both the actor and the critic.
We demonstrate our algorithm on seven 2D robot control tasks, with the most complex one being a differentiable half cheetah with hard contact constraints.
Empirical results show that our method boosts the performance of DDPGwithout sacrificing its robustness to local minima.

assistant: text: We propose a novel method that leverages the gradients from differentiable simulators to improve the performance of RL for robotics control
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reanalysis of Variance Reduced Temporal Difference Learning

Paper content:
Temporal difference (TD) learning is a popular algorithm for policy evaluation in reinforcement learning, but the vanilla TD can substantially suffer from the inherent optimization variance.
A variance reduced TD (VRTD) algorithm was proposed by Korda and La (2015), which applies the variance reduction technique directly to the online TD learning with Markovian samples.
In this work, we first point out the technical errors in the analysis of VRTD in Korda and La (2015), and then provide a mathematically solid analysis of the non-asymptotic convergence of VRTD and its variance reduction performance.
We show that VRTD is guaranteed to converge to a neighborhood of the fixed-point solution of TD at a linear convergence rate.
Furthermore, the variance error (for both i.i.d. and Markovian sampling) and the bias error (for Markovian sampling) of VRTD are significantly reduced by the batch size of variance reduction in comparison to those of vanilla TD.

assistant: text: This paper provides a rigorous study of the variance reduced TD learning and characterizes its advantage over vanilla TD learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Optimization on Multiple Manifolds

Paper content:
Optimization on manifold has been widely used in machine learning, to handle optimization problems with constraint.
Most previous works focus on the case with a single manifold.
However, in practice it is quite common that the optimization problem involves more than one constraints, (each constraint corresponding to one manifold).
It is not clear in general how to optimize on multiple manifolds effectively and provably especially when the intersection of multiple manifolds is not a manifold or cannot be easily calculated.
We propose a unified algorithm framework to handle the optimization on multiple manifolds.
Specifically,  we integrate information from multiple manifolds and move along an ensemble direction by viewing the information from each manifold as a drift and adding them together.
We prove the convergence properties of the proposed algorithms.
We also apply the algorithms into  training neural network with batch normalization layers and achieve preferable empirical results.

assistant: text: This paper introduces an algorithm to handle optimization problem with multiple constraints under vision of manifold.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models?

Paper content:
We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset.
The classifier is described by a nonlinear ReLU model and the objective function adopts the exponential loss function.
We first characterize the landscape of the loss function and show that there can exist spurious asymptotic local minima besides asymptotic global minima.
We then show that gradient descent (GD) can converge to either a global or a local max-margin direction, or may diverge from the desired max-margin direction in a general context.
For stochastic gradient descent (SGD), we show that it converges in expectation to either the global or the local max-margin direction if SGD converges.
We further explore the implicit bias of these algorithms in learning a multi-neuron network under certain stationary conditions, and show that the learned classifier maximizes the margins of each sample pattern partition under the ReLU activation.

assistant: text: We study the implicit bias of gradient methods in solving a binary classification problem with nonlinear ReLU models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exponentially Decaying Flows for Optimization in Deep Learning

Paper content:
The field of deep learning has been craving for an optimization method that shows outstanding property for both optimization and generalization.
We propose a method for mathematical optimization based on flows along geodesics, that is, the shortest paths between two points, with respect to the Riemannian metric induced by a non-linear function.
In our method, the flows refer to Exponentially Decaying Flows (EDF), as they can be designed to converge on the local solutions exponentially.
In this paper, we conduct experiments to show its high performance on optimization benchmarks (i.e., convergence properties), as well as its potential for producing good machine learning benchmarks (i.e., generalization properties).

assistant: text: Introduction of a new optimization method and its application to deep learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Few-Shot Regression via Learning Sparsifying Basis Functions

Paper content:
Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples.
Previous few-shot learning works have mainly focused on classification and reinforcement learning.
In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.
Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of sparsifying basis functions.
This enables a few labeled samples to approximate the function.
We design a Basis Function Learner network to encode basis functions for a task distribution, and a Weights Generator network to generate the weight vector for a novel task.
We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.

assistant: text: We propose a method of doing few-shot regression by learning a set of basis functions to represent the function distribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A FRAMEWORK  FOR ROBUSTNESS CERTIFICATION  OF SMOOTHED CLASSIFIERS USING  F-DIVERGENCES

Paper content:
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.
Although most techniques developed so far requires knowledge of the architecture of the machine learning model and remains hard to scale to complex prediction pipelines, the method of randomized smoothing has been shown to overcome many of these obstacles.
By requiring only black-box access to the underlying model, randomized smoothing scales to large architectures and is agnostic to the internals of the network.
However, past work on randomized smoothing has focused on restricted classes of smoothing measures or perturbations (like Gaussian or discrete) and has only been able to prove robustness with respect to simple norm bounds.
In this paper we introduce a general framework for proving robustness properties of smoothed machine learning models in the black-box setting.
Specifically, we extend randomized smoothing procedures to handle arbitrary smoothing measures and prove robustness of the smoothed classifier by using $f$-divergences.
Our methodology achieves state-of-the-art}certified robustness on MNIST, CIFAR-10 and ImageNet and also audio classification task, Librispeech, with respect to several classes of adversarial perturbations.

assistant: text: Develop a general framework to establish certified robustness of ML models against various classes of adversarial perturbations
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Learning Approximation: Zero-Shot Neural Network Speedup

Paper content:
Neural networks offer high-accuracy solutions to a range of problems, but are computationally costly to run in production systems.
We propose a technique called Deep Learning Approximation to take an already-trained neural network model and build a faster (and almost equally accurate) network by manipulating the network structure and coefficients without requiring re-training or access to the training data.
Speedup is achieved by applying a sequential series of independent optimizations that reduce the floating-point operations (FLOPs) required to perform a forward pass.
An optimal lossy approximation is chosen for each layer by weighing the relative accuracy loss and FLOP reduction.
On PASCAL VOC 2007 with the YOLO network, we show an end-to-end 2x speedup in a network forward pass with a $5$\% drop in mAP that can be re-gained by finetuning, enabling this network (and others like it) to be deployed in compute-constrained systems.

assistant: text: Decompose weights to use fewer FLOPs with SVD
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating Gradients for Discrete Random Variables by Sampling without Replacement

Paper content:
We derive an unbiased estimator for expectations over discrete random variables based on sampling without replacement, which reduces variance as it avoids duplicate samples.
We show that our estimator can be derived as the Rao-Blackwellization of three different estimators.
Combining our estimator with REINFORCE, we obtain a policy gradient estimator and we reduce its variance using a built-in control variate which is obtained without additional model evaluations.
The resulting estimator is closely related to other gradient estimators.
Experiments with a toy problem, a categorical Variational Auto-Encoder and a structured prediction problem show that our estimator is the only estimator that is consistently among the best estimators in both high and low entropy settings.

assistant: text: We derive a low-variance, unbiased gradient estimator for expectations over discrete random variables based on sampling without replacement
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages.
When people learn a foreign language, their pronunciation often reflect their native language's characteristics.
That motivates us to investigate how the speech synthesis network learns the pronunciation when multi-lingual dataset is given.
In this study, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages.
Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages.
Based on the result, we also show that it is possible to train networks that synthesize English speaker's Korean speech and vice versa.
In another experiment, we train the network with limited amount of English dataset and large Korean dataset, and analyze the required amount of dataset to train a resource-poor language with the help of resource-rich languages.
every speaker in the trained model could speak both English and Korean fluently.
We investigated 37 whether the phoneme embeddings from different languages are learned meaningful representations.
We found phonemes with similar pronunciation tend to stay closer than the others even across the 39 different languages.
From these results, we thought that the cross-lingual model would be possible to 40 generalize for a language with scarce amount of data when there is another language with abundant 41 data.
We trained cross-lingual TTS models while differing the amount of data for a resource-scarce 42 language.
Then we computed and compared character error rate (CER) of generated speeches from 43 each model by crowd-sourced human dictation.
To summarize, the contributions of this study are as follows: 45 1.
We successfully trained a cross-lingual multi-speaker TTS model using English and Korean 46 data in which no bilingual speaker is included.
3.
We showed how much data of a language is required to train a TTS model when we have a 50 large amount of data from another language.
DISPLAYFORM0 The 4 Spectrogram of generated speech using English phonemes and the nearest Korean phonemes using crowd-sourcing platform, and the average CER of the transcriptions is reported in TAB2 .
Also, the generated samples from each model are posted in the demo page.
The symbols in the parentheses are IPA symbols.
The symbol '-' in the parentheses denotes that there
was no IPA symbol for that phoneme.184 TAB2 aa (a) AE2 (ae) AY0 (aI) AW0 (aU) AW1 (aU) DISPLAYFORM0 The symbols in the parentheses are IPA symbols.
DISPLAYFORM1

assistant: text: Learned phoneme embeddings of multilingual neural speech synthesis network could represent relations of phoneme pronunciation between the languages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Generalization Effects of DenseNet Model Structures 

Paper content:
Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance.
While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied.
Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures' implicit regularization properties?
In this work, we investigate the skip connection's effect on network's generalization features.
Through experiments, we show that certain neural network architectures contribute to their generalization abilities.
Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with 'skip connections'.
We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.

assistant: text: Our paper analyses the tremendous representational power of networks especially with 'skip connections', which may be used as a method  for better generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction

Paper content:
Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations.
However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (ER), can change, rendering the intended targets of user feedback unknown–a problem we term identity uncertainty.
In this work, we present a framework for integrating user feedback into KBs in the presence of identity uncertainty.
Our approach is based on having user feedback participate alongside mentions in ER.
We propose a specific representation of user feedback as feedback mentions and introduce a new online algorithm for integrating these mentions into an existing KB.
In experiments, we demonstrate that our proposed approach outperforms the baselines in 70% of experimental conditions.

assistant: text: This paper develops a framework for integrating user feedback under identity uncertainty in knowledge bases.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Understanding Generalization in Gradient-Based Meta-Learning

Paper content:
In this work we study generalization of neural networks in gradient-based meta-learning by analyzing various properties of the objective landscapes.
We experimentally demonstrate that as meta-training progresses, the meta-test solutions obtained by adapting the meta-train solution of the model to new tasks via few steps of gradient-based fine-tuning, become flatter, lower in loss, and further away from the meta-train solution.
We also show that those meta-test solutions become flatter even as generalization starts to degrade, thus providing an experimental evidence against the correlation between generalization and flat minima in the paradigm of gradient-based meta-leaning.
Furthermore, we provide empirical evidence that generalization to new tasks is correlated with the coherence between their adaptation trajectories in parameter space, measured by the average cosine similarity between task-specific trajectory directions, starting from a same meta-train solution.
We also show that coherence of meta-test gradients, measured by the average inner product between the task-specific gradient vectors evaluated at meta-train solution, is also correlated with generalization.

assistant: text: We study generalization of neural networks in gradient-based meta- learning by analyzing various properties of the objective landscape.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bootstrapping the Expressivity with Model-based Planning

Paper content:
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.
We show, theoretically and empirically, that even for one-dimensional continuous state space, there are many MDPs whose optimal $Q$-functions and policies are much more complex than the dynamics.
We hypothesize many real-world MDPs also have a similar property.
For these MDPs, model-based planning is a favorable algorithm, because the resulting policies can approximate the optimal policy significantly better than a neural network parameterization can, and model-free or model-based policy optimization rely on policy parameterization.
Motivated by the theory, we apply a simple multi-step model-based bootstrapping planner (BOOTS) to bootstrap a weak $Q$-function into a stronger policy.
Empirical results show that applying BOOTS on top of model-based or model-free policy optimization algorithms at the test time improves the performance on MuJoCo benchmark tasks.

assistant: text: We compare deep model-based and model-free RL algorithms by studying the approximability of $Q$-functions, policies, and dynamics by neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: S-Flow GAN

Paper content:
Our work offers a new method for domain translation from semantic label maps
and Computer Graphic (CG) simulation edge map images to photo-realistic im-
ages.
We train a Generative Adversarial Network (GAN) in a conditional way to
generate a photo-realistic version of a given CG scene.
Existing architectures of
GANs still lack the photo-realism capabilities needed to train DNNs for computer
vision tasks, we address this issue by embedding edge maps, and training it in an
adversarial mode.
We also offer an extension to our model that uses our GAN
architecture to create visually appealing and temporally coherent videos.

assistant: text: Simulation to real images translation and video generation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Atomic Compression Networks

Paper content:
Compressed forms of deep neural networks are essential in deploying large-scale
computational models on resource-constrained devices.
Contrary to analogous
domains where large-scale systems are build as a hierarchical repetition of small-
scale units, the current practice in Machine Learning largely relies on models with
non-repetitive components.
In the spirit of molecular composition with repeating
atoms, we advance the state-of-the-art in model compression by proposing Atomic
Compression Networks (ACNs), a novel architecture that is constructed by recursive
repetition of a small set of neurons.
In other words, the same neurons with the
same weights are stochastically re-positioned in subsequent layers of the network.
Empirical evidence suggests that ACNs achieve compression rates of up to three
orders of magnitudes compared to fine-tuned fully-connected neural networks (88×
to 1116× reduction) with only a fractional deterioration of classification accuracy
(0.15% to 5.33%).
Moreover our method can yield sub-linear model complexities
and permits learning deep ACNs with less parameters than a logistic regression
with no decline in classification accuracy.

assistant: text: We advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Binarized Neural networks (BNNs) have been shown to be effective in improving network efficiency during the inference phase, after the network has been trained.
However, BNNs only binarize the model parameters and activations during propagations.
Therefore, BNNs do not offer significant efficiency improvements during training, since the gradients are still propagated and used with high precision.
We show there is no inherent difficulty in training BNNs using "Binarized BackPropagation" (BBP), in which we also binarize the gradients.
To avoid significant degradation in test accuracy, we simply increase the number of filter maps in a each convolution layer.
Using BBP on dedicated hardware can potentially significantly improve the execution efficiency (\emph{e.g.}, reduce dynamic memory footprint, memory bandwidth and computational energy) and speed up the training process with an appropriate hardware support, even after such an increase in network size.
Moreover, our method is ideal for distributed learning as it reduces the communication costs significantly (e.g., by ~32).
Using this method, we demonstrate a minimal loss in classification accuracy on several datasets and topologies.

assistant: text: Binarized Back-Propagation all you need for completely binarized training is to is to inflate the size of the network
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Instant Quantization of Neural Networks using Monte Carlo Methods

Paper content:
Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption.
We propose to apply Monte Carlo methods and importance sampling to sparsify and quantize pre-trained neural networks without any retraining.
We obtain sparse, low bit-width integer representations that approximate the full precision weights and activations.
The precision, sparsity, and complexity are easily configurable by the amount of sampling performed.
Our approach, called Monte Carlo Quantization (MCQ), is linear in both time and space, while the resulting quantized sparse networks show minimal accuracy loss compared to the original full-precision networks.
Our method either outperforms or achieves results competitive with methods that do require additional training on a variety of challenging tasks.

assistant: text: Monte Carlo methods for quantizing pre-trained models without any additional training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning World Graph Decompositions To Accelerate Reinforcement Learning

Paper content:
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.
We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them
.  Our framework has two learning phases
: 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and
2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions.
We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.

assistant: text: We learn a task-agnostic world graph abstraction of the environment and show how using it for structured exploration can significantly accelerate downstream task-specific RL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: From Here to There: Video Inbetweening Using Direct 3D Convolutions

Paper content:
We consider the problem of generating plausible and diverse video sequences, when we are only given a start and an end frame.
This task is also known as inbetweening, and it belongs to the broader area of stochastic video generation, which is generally approached by means of recurrent neural networks (RNN).
In this paper, we propose instead a fully convolutional model to generate video sequences directly in the pixel domain.
We first obtain a latent video representation using a stochastic fusion mechanism that learns how to incorporate information from the start and end frames.
Our model learns to produce such latent representation by progressively increasing the temporal resolution, and then decode in the spatiotemporal domain using 3D convolutions.
The model is trained end-to-end by minimizing an adversarial loss.
Experiments on several widely-used benchmark datasets show that it is able to generate meaningful and diverse in-between video sequences, according to both quantitative and qualitative evaluations.

assistant: text: This paper presents method for stochastically generating in-between video frames from given key frames, using direct 3D convolutions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Time Limits in Reinforcement Learning

Paper content:
In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes.
The task that the agent has to learn can either be to maximize its performance over
(i) that fixed amount of time, or
(ii) an indefinite period where the time limit is only used during training.
In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases.
In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent's input.
In the second case, the time limits are not part of the environment and are only used to facilitate learning.
We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode.
To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains.
Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms.

assistant: text: We consider the problem of learning optimal policies in time-limited and time-unlimited domains using time-limited interactions.