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

Title: Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention

Paper content:
Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain.
It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training.
When paired training data is not accessible, image translation becomes an ill-posed problem.
We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term.
We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input.
Thus we propose to add an attention module to predict an attention map to guide the image translation process.
The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes.
The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection.
Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
We propose to use a simple model with attention for image translation and domain adaption and achieve superior performance in a variety of tasks demonstrated by both qualitative and quantitative measures.
We show that the attention module is particularly helpful to focus the translation on region of interest, remove unwanted changes or artifacts, and may also be used for unsupervised segmentation or saliency detection.

assistant: text: We propose a simple generative model for unsupervised image translation and saliency detection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to predict visual brain activity by predicting future sensory states

Paper content:
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states.
We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex.
We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project (Cichy et al., 2019).
In contrast to previous findings in the literature (Khaligh-Razavi & Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos without further fine-tuning may outperform supervised image classification baselines in terms of correlation to spatial (fMRI) and temporal (MEG) data.

assistant: text: We show empirical evidence that predictive coding models yield representations more correlated to brain data than supervised image recognition models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Not-So-Random Features

Paper content:
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.
Our method produces a sequence of feature maps, iteratively refining the SVM margin.
We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.
Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.

assistant: text: A simple and practical algorithm for learning a margin-maximizing translation-invariant or spherically symmetric kernel from training data, using tools from Fourier analysis and regret minimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out

Paper content:
In natural language inference, the semantics of some words do not affect the inference.
Such information is considered superficial and brings overfitting.
How can we represent and discard such superficial information?
In this paper, we use first order logic (FOL) - a classic technique from meaning representation language – to explain what information is superficial for a given sentence pair.
Such explanation also suggests two inductive biases according to its properties.
We proposed a neural network-based approach that utilizes the two inductive biases.
We obtain substantial improvements over extensive experiments.

assistant: text: We use neural networks to project superficial information out for natural language inference by defining and identifying the superficial information from the perspective of first-order logic.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title:  The relativistic discriminator: a key element missing from standard GAN

Paper content:
In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.
The generator is trained to increase the probability that fake data is real.
We argue that it should also simultaneously decrease the probability that real data is real because
1) this would account for a priori knowledge that half of the data in the mini-batch is fake,
2) this would be observed with divergence minimization, and
3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs.
We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.
We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.
We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).
We show that IPM-based GANs are a subset of RGANs which use the identity function.
Empirically, we observe that
1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,
2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and
3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.
The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.

assistant: text: The paper proposes a “relativistic discriminator”, whic helps in some settings, although a bit sensitive to hyperparameters, architectures, and datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational lower bounds on mutual information based on nonextensive statistical mechanics

Paper content:
This paper aims to address the limitations of mutual information estimators based on variational optimization.
By redefining the cost using generalized functions from nonextensive statistical mechanics we raise the upper bound of previous estimators and enable the control of the bias variance trade off.
Variational based estimators outperform previous methods especially in high dependence high dimensional scenarios found in machine learning setups.
Despite their performance, these estimators either exhibit a high variance or are upper bounded by log(batch size).
Our approach inspired by nonextensive statistical mechanics uses different generalizations for the logarithm and the exponential in the partition function.
This enables the estimator to capture changes in mutual information over a wider range of dimensions and correlations of the input variables whereas previous estimators saturate them.

assistant: text: This paper tries to establish novel variational lower bounds for mutual information by introducing parameter q and defining q-algebra, showing that the lower bounds have smaller variance and achieves high values.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TequilaGAN: How To Easily Identify GAN Samples

Paper content:
In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.
One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.
The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.
We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.
We provide results on MNIST, CIFAR10, music and speech data.

assistant: text: We show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Preliminary theoretical troubleshooting in Variational Autoencoder

Paper content:
What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE?
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
On intrinsic dimension issue, due to information conservation, the idealistic VAE learns and only learns intrinsic factor dimension.
Besides, suggested by mutual information separation property, the constraint induced by Gaussian prior to the VAE objective encourages the information sparsity in dimension.
On disentanglement issue,   subsequently, inspired by information conservation theorem the clarification on disentanglement in this paper is made.
On real factor issue, due to factor equivalence, the idealistic VAE possibly learns any factor set in the equivalence class.
On indicator issue, the behavior of current disentanglement metric is discussed, and several performance indicators regarding the disentanglement and generating influence are subsequently raised to evaluate the performance of VAE model and to supervise the used factors.
On implementation issue, the experiments under noise modeling and constraints empirically testify the theoretical analysis and also show their own characteristic in pursuing disentanglement.

assistant: text: Studies the importance of the noise modelling in Gaussian VAE and proposes to train the noise using Empirical-Bayes like fashion.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Loss Scaling for Mixed Precision Training

Paper content:
Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.
MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.
Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.
We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.
We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.
We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.

assistant: text: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Inferring hierarchies of latent features in calcium imaging data

Paper content:
A key problem in neuroscience and life sciences more generally is that the data generation process is often best thought of as a hierarchy of dynamic systems.
One example of this is in-vivo calcium imaging data, where observed calcium transients are driven by a combination of electro-chemical kinetics where hypothesized trajectories around manifolds determining the frequency of these transients.
A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamic structure of reaching behaviour from spiking data modelled as a Poisson process.
Here we extend this approach using a ladder method to infer the spiking events driving calcium transients along with the deeper latent dynamic system.
We show strong performance of this approach on a benchmark synthetic dataset against a number of alternatives.
In-vivo two-photon calcium imaging provides systems neuroscientists with the ability to observe the activity of hundreds of neurons simultaneously during behavioural experiments.
Such highdimensional data is ripe for techniques identifying low-dimensional latent factors driving neural dynamics.
The most common methods, such as principal components analysis, ignore non-linearity and temporal dynamics in brain activity.
Pandarinath et al. (2018) [1] developed a new technique using deep, recurrent, variational auto-encoders which they named Latent Factor Analysis via Dynamical Systems (LFADS).
Using LFADS they found non-linear, dynamic latent variables describing highdimensional activity in the motor cortex that can decode reaching behaviour with much higher fidelity than other methods.
However, LFADS was designed for application to spiking data recorded from extracellular electrodes, not for two-photon calcium imaging data.
Two-photon calcium imaging poses the additional problem of identifying latent spike trains in fluorescence traces.
If we continue to model the frequency of events as being generated by a Poisson process, this can be seen as hierarchy of dynamic systems (Fig 1A) , in which low dimensional dynamics generate spike train probabilities that drive fluctuations in biophysical dynamics of calcium activity (Fig 1B.
Here we propose a method that extends LFADS to accommodate calcium activity using this hierarchical dynamic systems approach.

assistant: text: We propose an extension to LFADS capable of inferring spike trains to reconstruct calcium fluorescence traces using hierarchical VAEs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

Paper content:
Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory.
In a seminal paper, Telgarsky high- lighted the benefits of depth by presenting a family of functions (based on sim- ple triangular waves) for which DNNs achieve zero classification error, whereas shallow networks with fewer than exponentially many nodes incur constant error.
Even though Telgarsky’s work reveals the limitations of shallow neural networks, it doesn’t inform us on why these functions are difficult to represent and in fact he states it as a tantalizing open question to characterize those functions that cannot be well-approximated by smaller depths.
In this work, we point to a new connection between DNNs expressivity and Sharkovsky’s Theorem from dynamical systems, that enables us to characterize the depth-width trade-offs of ReLU networks for representing functions based on the presence of a generalized notion of fixed points, called periodic points (a fixed point is a point of period 1).
Motivated by our observation that the triangle waves used in Telgarsky’s work contain points of period 3 – a period that is special in that it implies chaotic behaviour based on the celebrated result by Li-Yorke – we proceed to give general lower bounds for the width needed to represent periodic functions as a function of the depth.
Technically, the crux of our approach is based on an eigenvalue analysis of the dynamical systems associated with such functions.

assistant: text: The authors derive depth-width tradeoff conditions for when relu networks are able to represent periodic functions using dynamical systems analysis.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization

Paper content:
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries.
However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions.
To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, harmonizing hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation.
By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior.
Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.

assistant: text: We pool messages amongst multiple SMILES strings of the same molecule to pass information along all paths through the molecular graph, producing latent representations that significantly surpass the state-of-the-art in a variety of tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scalable Neural Learning for Verifiable Consistency with Temporal Specifications

Paper content:
Formal verification of machine learning models has attracted attention recently, and significant progress has been made on proving simple properties like robustness to small perturbations of the input features.
In this context, it has also been observed that folding the verification procedure into training makes it easier to train verifiably robust models.
In this paper, we extend the applicability of verified training by extending it to (1) recurrent neural network architectures and (2) complex specifications that go beyond simple adversarial robustness, particularly specifications that capture temporal properties like requiring that a robot periodically visits a charging station or that a language model always produces sentences of bounded length.
Experiments show that while models trained using standard training often violate desired specifications, our verified training method produces models that both perform well (in terms of test error or reward) and can be shown to be provably consistent with specifications.

assistant: text: A way to train time-series regressors verifiably with respect to a set of rules defined by signal temporal logic, and work in deriving bound propagation rules for the STL language.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PDE-Net: Learning PDEs from Data

Paper content:
Partial differential equations (PDEs)  play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc.
PDEs are commonly derived based on physical laws or empirical observations.
However, the governing equations for many complex systems in modern applications are still not fully known.
With the rapid development of sensors, computational power, and data storage in the past decade, huge quantities of data can be easily collected and efficiently stored.
Such vast quantity of data offers new opportunities for data-driven discovery of hidden physical laws.
Inspired by the latest development of neural network designs in deep learning, we propose a new feed-forward deep network, called PDE-Net, to fulfill two objectives at the same time: to accurately predict dynamics of complex systems and to uncover the underlying hidden PDE models.
The basic idea of the proposed PDE-Net is to learn differential operators by learning convolution kernels (filters), and apply neural networks or other machine learning methods to approximate the unknown nonlinear responses.
Comparing with existing approaches, which either assume the form of the nonlinear response is known or fix certain finite difference approximations of differential operators, our approach has the most flexibility by learning both differential operators and the nonlinear responses.
A special feature of the proposed PDE-Net is that all filters are properly constrained, which enables us to easily identify the governing PDE models while still maintaining the expressive and predictive power of the network.
These constrains are carefully designed by fully exploiting the relation between the orders of differential operators and the orders of sum rules of filters (an important concept originated from wavelet theory).
We also discuss relations of the PDE-Net with some existing networks in computer vision such as Network-In-Network (NIN) and Residual Neural Network (ResNet).
Numerical experiments show that the PDE-Net has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.

assistant: text: The paper proposes a neural network based algorithm for learning from data that arises from dynamical systems with governing equations that can be written as partial differential equations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING

Paper content:
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.
Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.
In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.
TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.
We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.

assistant: text: Studies few-host learning in a transductive setting: using meta learning to learn to propagate labels from training samples to test samples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Emergent Communication through Negotiation

Paper content:
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems.
In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction.
We introduce two communication protocols - one grounded in the semantics of the game, and one which is a priori ungrounded.
We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded, cheap talk channel to do the same.
However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge.
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

assistant: text: Examines problems of how agents can use communication to maximise their rewards in a simple negotiation game.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MORTY Embedding: Improved Embeddings without Supervision

Paper content:
We demonstrate a low effort method that unsupervisedly constructs task-optimized embeddings from existing word embeddings to gain performance on a supervised end-task.
This avoids additional labeling or building more complex model architectures by instead providing specialized embeddings better fit for the end-task(s).
Furthermore, the method can be used to roughly estimate whether a specific kind of end-task(s) can be learned form, or is represented in, a given unlabeled dataset, e.g. using publicly available probing tasks.
We evaluate our method for diverse word embedding probing tasks and by size of embedding training corpus -- i.e. to explore its use in reduced (pretraining-resource) settings.

assistant: text: Morty refits pretrained word embeddings to either: (a) improve overall embedding performance (for Multi-task settings) or improve Single-task performance, while requiring only minimal effort.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Goal-Conditioned Video Prediction

Paper content:
Many processes can be concisely represented as a sequence of events leading from a starting state to an end state.
Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe.
Building upon this intuition,  we propose a new class of visual generative models: goal-conditioned predictors (GCP).
Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.
GCP instead treats videos as start-goal transformations, making video generation easier by conditioning on the more informative context provided by the first and final frames.
Not only do existing forward prediction approaches synthesize better and longer videos when modified to become goal-conditioned,  but GCP models can also utilize structures that are not linear in time, to accomplish hierarchical prediction
. To this end, we study both auto-regressive GCP models and novel tree-structured GCP models that generate frames recursively, splitting the video iteratively into finer and finer segments delineated by subgoals
. In experiments across simulated and real datasets, our GCP methods generate high-quality sequences over long horizons
.  Tree-structured GCPs are also substantially easier to parallelize than auto-regressive GCPs, making training  and  inference  very  efficient, and allowing the model to train on sequences that are thousands of frames in length.Finally, we demonstrate the utility of GCP approaches for imitation learning in the setting without access to expert actions
. Videos are on the supplementary website: https://sites.google.com/view/video-gcp

assistant: text: This paper reformulates video prediction problem as interpolation instead of extrapolation by conditioning the prediction on the start and end (goal) frame, resulting in higher quality predictions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Paper content:
Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications.
Many of these advances leverage the availability of large corpora.
While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora.
This paper aims to quantify and reduce biases exhibited by language models.
Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation.
We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature.
Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data.
We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.

assistant: text: A method based on semantic simiilarity and a method based on sentiment similarity to debias the neural language models trained from large datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Aggregating Crowdsourced Labels in Subjective Domains

Paper content:
Supervised learning problems---particularly those involving social data---are often subjective.
That is, human readers, looking at the same data, might come to legitimate but completely different conclusions based on their personal experiences.
Yet in machine learning settings feedback from multiple human annotators is often reduced to a single ``ground truth'' label, thus hiding the true, potentially rich and diverse interpretations of the data found across the social spectrum.
We explore the rewards and challenges of discovering and learning representative distributions of the labeling opinions of a large human population.
A major, critical cost to this approach is the number of humans needed to provide enough labels not only to obtain representative samples but also to train a machine to predict representative distributions on unlabeled data.
We propose aggregating label distributions over, not just individuals, but also data items, in order to maximize the costs of humans in the loop.
We test different aggregation approaches on state-of-the-art deep learning models.
Our results suggest that careful label aggregation methods can greatly reduce the number of samples needed to obtain representative distributions.

assistant: text: We study the problem of learning to predict the underlying diversity of beliefs present in supervised learning domains.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

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

assistant: text: The authors use multiple techniques/tools to enable neural timbre transfer (converting music from one instrument to another) without paired training examples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Novelty Search in representational space for sample efficient exploration

Paper content:
We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives.
Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational space to gauge novelty.
We then leverage these intrinsic rewards for sample-efficient exploration with planning routines in representational space.
One key element of our approach is that we perform more gradient steps in-between every environment step in order to ensure the model accuracy.
We test our approach on a number of maze tasks, as well as a control problem and show that our exploration approach is more sample-efficient compared to strong baselines.

assistant: text: This paper proposes a method for efficient exploration in tabular MDPs as well as a simple control environment, using deterministic encoders to learn a low dimensional representation of the environment dynamics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CONTROLLING COVARIATE SHIFT USING EQUILIBRIUM NORMALIZATION OF WEIGHTS

Paper content:
We introduce a new normalization technique that exhibits the fast convergence properties of batch normalization using a transformation of layer weights instead of layer outputs.
The proposed technique keeps the contribution of positive and negative weights to the layer output in equilibrium.
We validate our method on a set of standard benchmarks including CIFAR-10/100, SVHN and ILSVRC 2012 ImageNet.

assistant: text: An alternative normalization technique to batch normalization
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning with Latent Embedding Optimization

Paper content:
Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems.
However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes.
We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space.
The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters.
Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks.
Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

assistant: text: Latent Embedding Optimization (LEO) is a novel gradient-based meta-learner with state-of-the-art performance on the challenging 5-way 1-shot and 5-shot miniImageNet and tieredImageNet classification tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Once for All: Train One Network and Specialize it for Efficient Deployment

Paper content:
We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.
Most of the traditional approaches either manually design or use Neural Architecture Search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Our key idea is to decouple model training from architecture search to save the cost.
To this end, we propose to train a once-for-all network (OFA) that supports diverse architectural settings (depth, width, kernel size, and resolution).
Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.
To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks ($> 10^{19}$) simultaneously.
Extensive experiments on various hardware platforms (CPU, GPU, mCPU, mGPU, FPGA accelerator) show that OFA consistently outperforms SOTA NAS methods (up to 4.0% ImageNet top1 accuracy improvement over MobileNetV3) while reducing orders of magnitude GPU hours and $CO_2$ emission.
In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting ($<$600M FLOPs).
Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.

assistant: text: Method results in a network from which one can extract sub-networks for various resouce constraints (latency, memory) which perform well without a need for retraining.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Non-Autoregressive Dialog State Tracking

Paper content:
Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself.
These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values.
However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns.
In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots.
In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level.
Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.

assistant: text: We propose the first non-autoregressive neural model for Dialogue State Tracking (DST), achieving the SOTA accuracy (49.04%) on MultiWOZ2.1 benchmark, and reducing inference latency by an order of magnitude.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Non-Autoregressive Dialog State Tracking

Paper content:
Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself.
These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values.
However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among \textit{(domain, slot)} pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns.
In this paper, we propose a novel framework of Non-Autoregressive Dialog State Tracking (NADST) which can factor in potential dependencies among domains and slots to optimize the models towards better prediction of dialogue states as a complete set rather than separate slots.
In particular, the non-autoregressive nature of our method not only enables decoding in parallel to significantly reduce the latency of DST for real-time dialogue response generation, but also detect dependencies among slots at token level in addition to slot and domain level.
Our empirical results show that our model achieves the state-of-the-art joint accuracy across all domains on the MultiWOZ 2.1 corpus, and the latency of our model is an order of magnitude lower than the previous state of the art as the dialogue history extends over time.

assistant: text: A new model for the DST task that reduces inference time complexity with a non-autoregressive decoder, obtains competitive DST accuracy, and shows improvements over other baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title:  The relativistic discriminator: a key element missing from standard GAN

Paper content:
In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.
The generator is trained to increase the probability that fake data is real.
We argue that it should also simultaneously decrease the probability that real data is real because
1) this would account for a priori knowledge that half of the data in the mini-batch is fake,
2) this would be observed with divergence minimization, and
3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs.
We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.
We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.
We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).
We show that IPM-based GANs are a subset of RGANs which use the identity function.
Empirically, we observe that
1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,
2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and
3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.
The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.

assistant: text: Improving the quality and stability of GANs using a relativistic discriminator; IPM GANs (such as WGAN-GP) are a special case.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph Spectral Regularization For Neural Network Interpretability

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

assistant: text: Highlights the contribution of graph spectral regularizer to the interpretability of neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analyzing Inverse Problems with Invertible Neural Networks

Paper content:
For many applications, in particular in natural science, the task is to
determine hidden system parameters from a set of measurements.
Often,
the forward process from parameter- to measurement-space is well-defined,
whereas the inverse problem is ambiguous: multiple parameter sets can
result in the same measurement.
To fully characterize this ambiguity, the full
posterior parameter distribution, conditioned on an observed measurement,
has to be determined.
We argue that a particular class of neural networks
is well suited for this task – so-called Invertible Neural Networks (INNs).
Unlike classical neural networks, which attempt to solve the ambiguous
inverse problem directly, INNs focus on learning the forward process, using
additional latent output variables to capture the information otherwise
lost.
Due to invertibility, a model of the corresponding inverse process is
learned implicitly.
Given a specific measurement and the distribution of
the latent variables, the inverse pass of the INN provides the full posterior
over parameter space.
We prove theoretically and verify experimentally, on
artificial data and real-world problems from medicine and astrophysics, that
INNs are a powerful analysis tool to find multi-modalities in parameter space,
uncover parameter correlations, and identify unrecoverable parameters.

assistant: text: The research paper proposes an invertible network with observations for posterior probability of complex input distributions with a theoretical valid bidirectional training scheme.