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

Title: UCB EXPLORATION VIA Q-ENSEMBLES

Paper content:
We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.
We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting.
We propose an exploration strategy based on upper-confidence bounds (UCB).
Our experiments show significant gains on the Atari benchmark.
Deep reinforcement learning seeks to learn mappings from high-dimensional observations to actions.
Deep Q-learning BID15 ) is a leading technique that has been used successfully, especially for video game benchmarks.
However, fundamental challenges remain, for example, improving sample efficiency and ensuring convergence to high quality solutions.
Provably optimal solutions exist in the bandit setting and for small MDPs, and at the core of these solutions are exploration schemes.
However these provably optimal exploration techniques do not extend to deep RL in a straightforward way.Bootstrapped DQN BID16 ) is a previous attempt at adapting a theoretically verified approach to deep RL.
In particular, it draws inspiration from posterior sampling for reinforcement learning (PSRL, BID17 ; BID16 ), which has near-optimal regret bounds.
PSRL samples an MDP from its posterior each episode and exactly solves Q * , its optimal Q-function.
However, in high-dimensional settings, both approximating the posterior over MDPs and solving the sampled MDP are intractable.
Bootstrapped DQN avoids having to establish and sample from the posterior over MDPs by instead approximating the posterior over Q * .
In addition, bootstrapped DQN uses a multi-headed neural network to represent the Q-ensemble.
While the authors proposed bootstrapping to estimate the posterior distribution, their empirical findings show best performance is attained by simply relying on different initializations for the different heads, not requiring the sampling-with-replacement process that is prescribed by bootstrapping.In this paper, we design new algorithms that build on the Q-ensemble approach from BID16 .
However, instead of using posterior sampling for exploration, we construct uncertainty estimates from the Q-ensemble.
Specifically, we first propose the Ensemble Voting algorithm where the agent takes action by a majority vote from the Q-ensemble.
Next, we propose the UCB exploration strategy.
This strategy is inspired by established UCB algorithms in the bandit setting and constructs uncertainty estimates of the Q-values.
In this strategy, agents are optimistic and take actions with the highest UCB.
We demonstrate that our algorithms significantly improve performance on the Atari benchmark.

assistant: text: Adapting UCB exploration to ensemble Q-learning improves over prior methods such as Double DQN, A3C+ on Atari benchmark
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The blood diamond effect in neural art: On ethically troublesome images of the imagenet dataset

Paper content:
The main goal of this short paper is to inform the neural art community at large on the ethical ramifications of using models trained on the imagenet dataset, or using seed images from classes 445 -n02892767- [’bikini, two-piece’] and 459- n02837789- [’brassiere, bra, bandeau’] of the same.
We discovered that many of the images belong to these classes were verifiably pornographic, shot in a non-consensual setting, voyeuristic and also entailed underage nudity.
Akin to the \textit{ivory carving-illegal poaching} and \textit{diamond jewelry art-blood diamond} nexuses, we posit there is a similar moral conundrum at play here and would like to instigate a conversation amongst the neural artists in the community.

assistant: text: There's non-consensual and pornographic images in the ImageNet dataset
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Latent Semantic Representation from Pre-defined Generative Model

Paper content:
Learning representations of data is an important issue in machine learning.
Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead.
GAN tends to produce the data simply without any information about the manifold of the data, which hinders from controlling desired features to generate.
Moreover, most of GAN’s have a large size of manifold, resulting in poor scalability.
In this paper, we propose a novel GAN to control the latent semantic representation, called LSC-GAN, which allows us to produce desired data to generate and learns a representation of the data efficiently.
Unlike the conventional GAN models with hidden distribution of latent space, we define the distributions explicitly in advance that are trained to generate the data based on the corresponding features by inputting the latent variables that follow the distribution.
As the larger scale of latent space caused by deploying various distributions in one latent space makes training unstable while maintaining the dimension of latent space, we need to separate the process of defining the distributions explicitly and operation of generation.
We prove that a VAE is proper for the former and modify a loss function of VAE to map the data into the pre-defined latent space so as to locate the reconstructed data as close to the input data according to its characteristics.
Moreover, we add the KL divergence to the loss function of LSC-GAN to include this process.
The decoder of VAE, which generates the data with the corresponding features from the pre-defined latent space, is used as the generator of the LSC-GAN.
Several experiments on the CelebA dataset are conducted to verify the usefulness of the proposed method to generate desired data stably and efficiently, achieving a high compression ratio that can hold about 24 pixels of information in each dimension of latent space.
Besides, our model learns the reverse of features such as not laughing (rather frowning) only with data of ordinary and smiling facial expression.

assistant: text: We propose a generative model that not only produces data with desired features from the pre-defined latent space but also fully understands the features of the data to create characteristics that are not in the dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Effective and Efficient Batch Normalization Using Few Uncorrelated Data for Statistics' Estimation

Paper content:
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role.
However, it has been observed that BN is costly due to the reduction operations.
In this paper, we propose alleviating the BN’s cost by using only a small fraction of data for mean & variance estimation at each iteration.
The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency.
We identify that the effectiveness expects less data correlation while the efficiency expects regular execution pattern.
To this end, we propose two categories of approach: sampling or creating few uncorrelated data for statistics’ estimation with certain strategy constraints.
The former includes “Batch Sampling (BS)” that randomly selects few samples from each batch and “Feature Sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “Virtual Dataset Normalization (VDN)” that generates few synthetic random samples.
Accordingly, multi-way strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime.
All the proposed methods are comprehensively evaluated on various DNN models, where an overall training speedup by up to 21.7% on modern GPUs can be practically achieved without the support of any specialized libraries, and the loss of model accuracy and convergence rate are negligible.
Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-batch normalization” problem in the case of tiny batch size.

assistant: text: We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations  with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Paper content:
Uncertainty estimation and ensembling methods go hand-in-hand.
Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance.
At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation.
In this work, we focus on in-domain uncertainty for image classification.
We explore the standards for its quantification and point out pitfalls of existing metrics.
Avoiding these pitfalls, we perform a broad study of different ensembling techniques.
To provide more insight in the broad comparison, we introduce the deep ensemble equivalent (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of very few independently trained networks in terms of the test log-likelihood.

assistant: text: We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Speech Enhancement with the Wave-U-Net

Paper content:
We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment.
This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information and being able to take large  temporal contexts into account.
Our experiments show that the proposed method improves several metrics, namely PESQ, CSIG, CBAK, COVL and SSNR, over the  state-of-the-art with respect to the speech enhancement task on the Voice Bank corpus (VCTK) dataset.
We find that a reduced number of hidden layers is sufficient for speech enhancement in comparison to the original system designed for singing voice separation in music.
We see this initial result as an encouraging signal to further explore speech enhancement in the time-domain, both as an end in itself and as a pre-processing step to speech recognition systems.

assistant: text: The Wave-U-Net architecture, recently introduced by Stoller et al for music source separation, is highly effective for speech enhancement, beating the state of the art.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BNN+: Improved Binary Network Training

Paper content:
Deep neural networks (DNN) are widely used in many applications.
However, their deployment on edge devices has been difficult because they are resource hungry.
Binary neural networks (BNN) help to alleviate the prohibitive resource requirements of DNN, where both activations and weights are limited to 1-bit.
We propose an improved binary training method (BNN+), by introducing a regularization function that encourages training weights around binary values.
In addition to this, to enhance model performance we add trainable scaling factors to our regularization functions.
Furthermore, we use an improved approximation of the derivative of the sign activation function in the backward computation.
These additions are based on linear operations that are easily implementable into the binary training framework.
We show experimental results on CIFAR-10 obtaining an accuracy of 86.5%, on AlexNet and 91.3% with VGG network.
On ImageNet, our method also outperforms the traditional BNN method and XNOR-net, using AlexNet by a margin of 4% and 2% top-1 accuracy respectively.

assistant: text: The paper presents an improved training mechanism for obtaining binary networks with smaller accuracy drop that helps close the gap with it's full precision counterpart
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning

Paper content:
We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks.
The basic approach is to learn a model patch - a small set of parameters - that will specialize to each task, instead of fine-tuning the last layer or the entire network.
For instance, we show that learning a set of scales and biases is sufficient to convert a pretrained network to perform well on qualitatively different problems (e.g. converting a Single Shot MultiBox Detection (SSD) model into a 1000-class image classification model while reusing 98% of parameters of the SSD feature extractor).
Similarly, we show that re-learning existing low-parameter layers (such as depth-wise convolutions) while keeping the rest of the network frozen also improves transfer-learning accuracy significantly.
Our approach allows both simultaneous (multi-task) as well as sequential transfer learning.
In several multi-task learning problems, despite using much fewer parameters than traditional logits-only fine-tuning, we match single-task performance.

assistant: text: A novel and practically effective method to adapt pretrained neural networks to new tasks by retraining a minimal (e.g., less than 2%) number of parameters
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BEHAVIOR MODULE IN NEURAL NETWORKS

Paper content:
Prefrontal cortex (PFC) is a part of the brain which is responsible for behavior repertoire.
Inspired by PFC functionality and connectivity,  as well as human behavior formation process, we propose a novel modular architecture of neural networks with a Behavioral Module (BM) and corresponding end-to-end training strategy.
This approach allows the efficient learning of behaviors and preferences representation.
This property is particularly useful for user modeling (as for dialog agents) and recommendation tasks, as allows learning personalized representations of different user states.
In the experiment with video games playing, the resultsshow that the proposed method allows separation of main task’s objectives andbehaviors between different BMs.
The experiments also show network extendability through independent learning of new behavior patterns.
Moreover, we demonstrate a strategy for an efficient transfer of newly learned BMs to unseen tasks.

assistant: text: Extendable Modular Architecture is proposed for developing of variety of Agent Behaviors in DQN.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Anomaly Detection with Outlier Exposure

Paper content:
It is important to detect anomalous inputs when deploying machine learning systems.
The use of larger and more complex inputs in deep learning magnifies the difficulty of distinguishing between anomalous and in-distribution examples.
At the same time, diverse image and text data are available in enormous quantities.
We propose leveraging these data to improve deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, an approach we call Outlier Exposure (OE).
This enables anomaly detectors to generalize and detect unseen anomalies.
In extensive experiments on natural language processing and small- and large-scale vision tasks, we find that Outlier Exposure significantly improves detection performance.
We also observe that cutting-edge generative models trained on CIFAR-10 may assign higher likelihoods to SVHN images than to CIFAR-10 images; we use OE to mitigate this issue.
We also analyze the flexibility and robustness of Outlier Exposure, and identify characteristics of the auxiliary dataset that improve performance.

assistant: text: OE teaches anomaly detectors to learn heuristics for detecting unseen anomalies; experiments are in classification, density estimation, and calibration in NLP and vision settings; we do not tune on test distribution samples, unlike previous work
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Paper content:
Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates.
Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates.
Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods.
In our paper, we demonstrate that extreme learning rates can lead to poor performance.
We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence.
We further conduct experiments on various popular tasks and models, which is often insufficient in previous work.
Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time.
Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks.
The implementation of the algorithm can be found at https://github.com/Luolc/AdaBound .

assistant: text: Novel variants of optimization methods that combine the benefits of both adaptive and non-adaptive methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL

Paper content:
We improve previous end-to-end differentiable neural networks (NNs) with fast
weight memories.
A gate mechanism updates fast weights at every time step of
a sequence through two separate outer-product-based matrices generated by slow
parts of the net.
The system is trained on a complex sequence to sequence variation
of the Associative Retrieval Problem with roughly 70 times more temporal
memory (i.e. time-varying variables) than similar-sized standard recurrent NNs
(RNNs).
In terms of accuracy and number of parameters, our architecture outperforms
a variety of RNNs, including Long Short-Term Memory, Hypernetworks,
and related fast weight architectures.

assistant: text: An improved Fast Weight network which shows better results on a general toy task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

Paper content:
It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples.
Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial perturbations, which introduces the globally linear behavior in-between training examples.
However, in previous work, the mixup-trained models only passively defend adversarial attacks in inference by directly classifying the inputs, where the induced global linearity is not well exploited.
Namely, since the locality of the adversarial perturbations, it would be more efficient to actively break the locality via the globality of the model predictions.
Inspired by simple geometric intuition, we develop an inference principle, named mixup inference (MI), for mixup-trained models.
MI mixups the input with other random clean samples, which can shrink and transfer the equivalent perturbation if the input is adversarial.
Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants.

assistant: text: We exploit the global linearity of the mixup-trained models in inference to break the locality of the adversarial perturbations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Low Bias Gradient Estimates for Very Deep Boolean Stochastic Networks

Paper content:
Stochastic neural networks with discrete random variables are an important class of models for their expressivity and interpretability.
Since direct differentiation and backpropagation is not possible, Monte Carlo gradient estimation techniques have been widely employed for training such models.
Efficient stochastic gradient estimators, such Straight-Through and Gumbel-Softmax, work well for shallow models with one or two stochastic layers.
Their performance, however, suffers with increasing model complexity.
In this work we focus on stochastic networks with multiple layers of Boolean latent variables.
To analyze such such networks, we employ the framework of harmonic analysis for Boolean functions.
We use it to derive an analytic formulation for the source of bias in the biased Straight-Through estimator.
Based on the analysis we propose \emph{FouST}, a simple gradient estimation algorithm that relies on three simple bias reduction steps.
Extensive experiments show that FouST performs favorably compared to state-of-the-art biased estimators, while being much faster than unbiased ones.
To the best of our knowledge FouST is the first gradient estimator to train up very deep stochastic neural networks, with up to 80 deterministic and 11 stochastic layers.

assistant: text: We present a low-bias estimator for Boolean stochastic variable models with many stochastic layers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lyceum: An efficient and scalable ecosystem for robot learning

Paper content:
We introduce Lyceum, a high-performance computational ecosystem for robotlearning.
Lyceum is built on top of the Julia programming language and theMuJoCo physics simulator, combining the ease-of-use of a high-level program-ming  language  with  the  performance  of  native  C.  Lyceum  is  up  to  10-20Xfaster  compared  to  other  popular  abstractions  like  OpenAI’sGymand  Deep-Mind’sdm-control.
This substantially reduces training time for various re-inforcement learning algorithms;  and is also fast enough to support real-timemodel  predictive  control  with  physics  simulators.
Lyceum  has  a  straightfor-ward API and supports parallel computation across multiple cores or machines.
The code base,  tutorials,  and demonstration videos can be found at: https://sites.google.com/view/lyceum-anon.

assistant: text: A high performance robotics simulation and algorithm development framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments

Paper content:
Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures?
We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018).
We fine-tune these encoders to do acceptability classification over CoLA and compare the models’ performance on the annotated analysis set.
Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.

assistant: text: We investigate the implicit syntactic knowledge of sentence embeddings using a new analysis set of grammatically annotated sentences with acceptability judgments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Causal Discovery with Reinforcement Learning

Paper content:
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences.
Traditional score-based casual discovery methods rely on various local heuristics to search for a Directed Acyclic Graph (DAG) according to a predefined score function.
While these methods, e.g., greedy equivalence search, may have attractive results with infinite samples and certain model assumptions, they are less satisfactory in practice due to finite data and possible violation of assumptions.
Motivated by recent advances in neural combinatorial optimization, we propose to use Reinforcement Learning (RL) to search for the DAG with the best scoring.
Our encoder-decoder model takes observable data as input and generates graph adjacency matrices that are used to compute rewards.
The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.
In contrast with typical RL applications where the goal is to learn a policy, we use RL as a search strategy and our final output would be the graph, among all graphs generated during training, that achieves the best reward.
We conduct experiments on both synthetic and real datasets, and show that the proposed approach not only has an improved search ability but also allows for a flexible score function under the acyclicity constraint.

assistant: text: We apply reinforcement learning to score-based causal discovery and achieve promising results on both synthetic and real datasets
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Countering Adversarial Images using Input Transformations

Paper content:
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system.
Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier.
Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images.
The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses.
Our best defense eliminates 60% of strong gray-box and 90% of strong black-box attacks by a variety of major attack methods.

assistant: text: We apply a model-agnostic defense strategy against adversarial examples and achieve 60% white-box accuracy and 90% black-box accuracy against major attack algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Autoencoder with Arbitrary Conditioning

Paper content:
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot".
The features may be both real-valued and categorical.
Training of the model is performed by stochastic variational Bayes.
The experimental evaluation on synthetic data, as well as feature imputation and image inpainting problems, shows the effectiveness of the proposed approach and diversity of the generated samples.

assistant: text: We propose an extension of conditional variational autoencoder that allows conditioning on an arbitrary subset of the features and sampling the remaining ones.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Scalable Risk-based Framework for Rigorous Autonomous Vehicle Evaluation

Paper content:
While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing.
Real-world testing, the de facto evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims.
We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms.
Using adaptive sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior.
We demonstrate our framework on a highway scenario.

assistant: text: Using adaptive sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: cGANs with Projection Discriminator

Paper content:
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
This approach is in contrast with most frameworks of conditional GANs used in application today, which use the conditional information by concatenating the (embedded) conditional vector to the feature vectors.
With this modification, we were able to significantly improve the quality of the class conditional image generation on ILSVRC2012 (ImageNet) dataset from the current state-of-the-art result, and we achieved this with a single pair of a discriminator and a generator.
We were also able to extend the application to super-resolution and succeeded in producing highly discriminative super-resolution images.
This new structure also enabled high quality category transformation based on parametric functional transformation of conditional batch normalization layers in the generator.

assistant: text: We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Depth-Recurrent Residual Connections for Super-Resolution of Real-Time Renderings

Paper content:
Inferring temporally coherent data features is crucial for a large variety of learning tasks.
We propose a network architecture that introduces temporal recurrent connections for the internal  state of the widely used residual blocks.
We demonstrate that, with these connections, convolutional neural networks can more robustly learn stable temporal states that persist between evaluations.
We demonstrate their potential for inferring high-quality super-resolution images from low resolution images produced with real-time renderers.
This data arises in a wide range of applications,  and is particularly challenging as it contains a strongly aliased signal.
Hence, the data differs substantially from the smooth inputs encountered  in natural videos, and existing techniques do not succeed at producing acceptable image quality.
We additionally propose a series of careful adjustments of typical generative adversarial architectures for video super-resolution to arrive at a first model that can produce detailed, yet temporally coherent images from an aliased stream of inputs from a real-time renderer.

assistant: text: A method for persistent latent states in ResBlocks demonstrated for super-resolution of alised image sequences.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamical Clustering of Time Series Data Using Multi-Decoder RNN Autoencoder

Paper content:
Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis.
The performance of a clustering algorithm depends on the features extracted from the data.
However, in time series analysis, there has been a problem that the conventional methods based on the signal shape are unstable for phase shift, amplitude and signal length variations.
In this paper, we propose a new clustering algorithm focused on the dynamical system aspect of the signal using recurrent neural network and variational Bayes method.
Our experiments show that our proposed algorithm has a robustness against above variations and boost the classification performance.

assistant: text: Novel time series data clustring algorithm based on dynamical system features.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Trace norm regularization and faster inference for embedded speech recognition RNNs

Paper content:
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR).
For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications.
Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models.
For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library.
Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.

assistant: text: We compress and speed up speech recognition models on embedded devices through a trace norm regularization technique and optimized kernels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MARGINALIZED AVERAGE ATTENTIONAL NETWORK FOR WEAKLY-SUPERVISED LEARNING

Paper content:
In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions.
To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner.
The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion.
MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features
. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others
. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos
. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from $O(2^T)$ to $O(T^2)$.
Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization.

assistant: text: A novel marginalized average attentional network for weakly-supervised temporal action localization
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Doubly Nested Network for Resource-Efficient Inference

Paper content:
We propose a new anytime neural network which allows partial evaluation by subnetworks with different widths as well as depths.
Compared to conventional anytime networks only with the depth controllability, the increased architectural diversity leads to higher resource utilization and consequent performance improvement under various and dynamic resource budgets.
We highlight architectural features to make our scheme feasible as well as efficient, and show its effectiveness in image classification tasks.

assistant: text: We propose a new anytime neural network which allows partial evaluation by subnetworks with different widths as well as depths.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Local Features for Improving the Generalization of Adversarial Training

Paper content:
Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples.
However, adversarially trained models often lack adversarially robust generalization on unseen testing data.
Recent works show that adversarially trained models are more biased towards global structure features.
Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation.
To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples.
We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples.
To demonstrate the generality of our argument, we implement RLFAT in currently state-of-the-art adversarial training frameworks.
Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training.
Additionally, we demonstrate that our models capture more local features of the object on the images, aligning better with human perception.

assistant: text: We propose a new stream of adversarial training approach called Robust Local Features for Adversarial Training (RLFAT) that significantly improves both the adversarially robust generalization and the standard generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Approximate Inference for Fully Bayesian Gaussian Process Regression

Paper content:
Learning in Gaussian Process models occurs through the adaptation of hyperparameters of the mean and the covariance function.
The classical approach entails maximizing the marginal likelihood yielding fixed point estimates (an approach called Type II maximum likelihood or ML-II).
An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call Fully Bayesian Gaussian Process Regression (GPR)
. This work considers two approximations to the intractable hyperparameter posterior
, 1) Hamiltonian Monte Carlo (HMC) yielding a sampling based approximation and
2) Variational Inference (VI) where the posterior over hyperparameters is approximated by a factorized Gaussian (mean-field) or a full rank Gaussian accounting for correlations between hyperparameters.
We analyse the predictive performance for fully Bayesian GPR on a range of benchmark data sets.

assistant: text: Analysis of Bayesian Hyperparameter Inference in Gaussian Process Regression
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering

Paper content:
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering.
Such an organization significantly constricts the types of shared structure that can be learned.
The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers.
The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks.
Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains.
These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.

assistant: text: Relaxing the constraint of shared hierarchies enables more effective deep multitask learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Relevance Prediction with Transfer Learning in Large-scale Retrieval Systems

Paper content:
Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance.
However, collecting users' explicit feedback is costly.
In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval systems.
Specifically, we adopt a two-tower neural net architecture to model query-item relevance given both collaborative and content information.
By introducing auxiliary tasks trained with much richer implicit user feedback data, we improve the quality and resolution for the learned representations of queries and items.
Applying these learned representations to an industrial retrieval system has delivered significant improvements.

assistant: text: We propose a novel two-tower shared-bottom model architecture for transferring knowledge from rich implicit feedbacks to predict relevance for large-scale retrieval systems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods

Paper content:
Adam is shown not being able to converge to the optimal solution in certain cases.
Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice.
In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods.
We argue that there exists an inappropriate correlation between gradient $g_t$ and the second moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size.
We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam.
Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$.
The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.

assistant: text: We analysis and solve the non-convergence issue of Adam.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learn to Pay Attention

Paper content:
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.
The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map.
Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parametrised by the score matrices, must alone be used for classification.
Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values.
Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter.
Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets.
When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset.
We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.

assistant: text: The paper proposes a method for forcing CNNs to leverage spatial attention in learning more object-centric representations that perform better in various respects.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning from Rules Generalizing Labeled Exemplars

Paper content:
In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision.
We propose a rule-exemplar model for collecting human supervision to combine the scalability of rules with the quality of instance labels.
The supervision is coupled such that it is both natural for humans and synergistic for learning.
We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables.
Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2)  
the coupled rule-exemplar supervision is effective in denoising rules.

assistant: text: Coupled rule-exemplar supervision and a implication loss helps to jointly learn to denoise rules and imply labels.