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

Title: A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision

Paper content:
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.
Previous works of MLL mainly focused on the setting where the concept set is assumed to be fixed, while many real-world applications require introducing new concepts into the set to meet new demands.
One common need is to refine the original coarse concepts and split them into finer-grained ones, where the refinement process typically begins with limited labeled data for the finer-grained concepts.
To address the need, we propose a special weakly supervised MLL problem that not only focuses on the situation of limited fine-grained supervision but also leverages the hierarchical relationship between the coarse concepts and the fine-grained ones.
The problem can be reduced to a multi-label version of negative-unlabeled learning problem using the hierarchical relationship.
We tackle the reduced problem with a meta-learning approach that learns to assign pseudo-labels to the unlabeled entries.
Experimental results demonstrate that our proposed method is able to assign accurate pseudo-labels, and in turn achieves superior classification performance when compared with other existing methods.

assistant: text: We propose a special weakly-supervised multi-label learning problem along with a newly tailored algorithm that learns the underlying classifier by learning to assign pseudo-labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Infer Graphics Programs from Hand-Drawn Images

Paper content:
We introduce a model that learns to convert simple hand drawings
  into graphics programs written in a subset of \LaTeX.~
The model
  combines techniques from deep learning and program synthesis.
We
  learn a convolutional neural network that proposes plausible drawing
  primitives that explain an image.
These drawing primitives are like
  a trace of the set of primitive commands issued by a graphics
  program.
We learn a model that uses program synthesis techniques to
  recover a graphics program from that trace.
These programs have
  constructs like variable bindings, iterative loops, or simple kinds
  of conditionals.
With a graphics program in hand, we can correct
  errors made by the deep network and extrapolate drawings.
Taken
  together these results are a step towards agents that induce useful,
  human-readable programs from perceptual input.

assistant: text: Learn to convert a hand drawn sketch into a high-level program
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-agent query reformulation: Challenges and the role of diversity

Paper content:
We investigate methods to efficiently learn diverse strategies in reinforcement learning for a generative structured prediction problem: query reformulation.
In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to produce a final answer.
Sub-agents are trained on disjoint partitions of the training data, while the meta-agent is trained on the full training set.
Our method makes learning faster, because it is highly parallelizable, and has better generalization performance than strong baselines, such as
an ensemble of agents trained on the full data.
We evaluate on the tasks of document retrieval and question answering.
The
improved performance seems due to the increased diversity of reformulation strategies.
This suggests that multi-agent, hierarchical approaches might play an important role in structured prediction tasks of this kind.
However, we also find that it is not obvious how to characterize diversity in this context, and a first attempt based on clustering did not produce good results.
Furthermore, reinforcement learning for the reformulation task is hard in high-performance regimes.
At best, it only marginally improves over the state of the art, which highlights the complexity of training models in this framework for end-to-end language understanding problems.

assistant: text: We use reinforcement learning for query reformulation on two tasks and surprisingly find that when training multiple agents diversity of the reformulations is more important than specialisation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

Paper content:
Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training data that can outperform more traditional regularized least squares solutions.
Along these lines, we present some extensions of the Neumann network, a recently introduced end-to-end learned architecture inspired by a truncated Neumann series expansion of the solution map to a regularized least squares problem.
Here we summarize the Neumann network approach, and show that it has a form compatible with the optimal reconstruction function for a given inverse problem.
We also investigate an extension of the Neumann network that incorporates a more sample efficient patch-based regularization approach.

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

Title: Cellular neuromodulation in artificial networks

Paper content:
Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack.
Such adaptation property strongly relies on cellular neuromodulation, the biological mechanism that dynamically controls neuron intrinsic properties and response to external stimuli in a context dependent manner.
In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours.
The network adaptation capabilities are tested on navigation benchmarks in a meta-learning context and compared with state-of-the-art approaches.
Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.

assistant: text: This paper introduces neuromodulation in artificial neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Attentive Weights Generation for Few Shot Learning via Information Maximization

Paper content:
Few shot image classification aims at learning a classifier from limited labeled data.
Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness.
However, we argue that it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples.
In this work, we introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which addresses current issues by two novel contributions.
i) AWGIM generates different classification weights for different query samples by letting each of query samples attends to the whole support set.
ii) To guarantee the generated weights adaptive to different query sample, we re-formulate the problem to maximize the lower bound of mutual information between generated weights and query as well as support data.
As far as we can see, this is the first attempt to unify information maximization into few shot learning.
Both two contributions are proved to be effective in the extensive experiments and we show that AWGIM is able to achieve state-of-the-art performance on benchmark datasets.

assistant: text: A novel few shot learning method to generate query-specific classification weights via information maximization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Latent Domain Transfer: Crossing modalities with Bridging Autoencoders

Paper content:
Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels.
However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video),
utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes.
Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces.
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network).
We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space.
The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.

assistant: text: Conditional VAE on top of latent spaces of pre-trained generative models that enables  transfer between drastically different domains while preserving locality and semantic alignment.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Seq2Slate: Re-ranking and Slate Optimization with RNNs

Paper content:
Ranking is a central task in machine learning and information retrieval.
In this task, it is especially important to present the user with a slate of items that is appealing as a whole.
This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be chosen alongside it.
In this work, we propose a sequence-to-sequence model for ranking called seq2slate.
At each step, the model predicts the next item to place on the slate given the items already chosen.
The recurrent nature of the model allows complex dependencies between items to be captured directly in a flexible and scalable way.
We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data.
We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.

assistant: text: A pointer network architecture for re-ranking items, learned from click-through logs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Structured Prediction using cGANs with Fusion Discriminator

Paper content:
We propose the fusion discriminator, a single unified framework for incorporating conditional information into a generative adversarial network (GAN) for a variety of distinct structured prediction tasks, including image synthesis, semantic segmentation, and depth estimation.
Much like commonly used convolutional neural network - conditional Markov random field (CNN-CRF) models, the proposed method is able to enforce higher-order consistency in the model, but without being limited to a very specific class of potentials.
The method is conceptually simple and flexible, and our experimental results demonstrate improvement on several diverse structured prediction tasks.

assistant: text: We propose the fusion discriminator, a novel architecture for incorporating conditional information into the discriminator of GANs for structured prediction tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers

Paper content:
Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation.
However, conditioning CNFs on signals of interest for conditional image generation and downstream predictive tasks is inefficient due to the high-dimensional latent code generated by the model, which needs to be of the same size as the input data.
In this paper, we propose InfoCNF, an efficient conditional CNF that partitions the latent space into a class-specific supervised code and an unsupervised code that shared among all classes for efficient use of labeled information.
Since the partitioning strategy (slightly) increases the number of function evaluations (NFEs),  InfoCNF also employs gating networks to learn the error tolerances of its ordinary differential equation (ODE) solvers for better speed and performance.
We show empirically that InfoCNF improves the test accuracy over the baseline  while yielding comparable likelihood scores and reducing the NFEs on CIFAR10.
Furthermore, applying the same partitioning strategy in InfoCNF on time-series data helps improve extrapolation performance.

assistant: text: We propose the InfoCNF, an efficient conditional CNF that employs gating networks to learn the error tolerances of the ODE solvers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO SHARE: SIMULTANEOUS PARAMETER TYING AND SPARSIFICATION IN DEEP LEARNING

Paper content:
Deep neural networks (DNNs) usually contain millions, maybe billions, of parameters/weights, making both storage and computation very expensive.
This has motivated a large body of work to reduce the complexity of the neural network by using sparsity-inducing regularizers.
Another well-known approach for controlling the complexity of DNNs is parameter sharing/tying, where certain sets of weights are forced to share a common value.
Some forms of weight sharing are hard-wired to express certain in- variances, with a notable example being the shift-invariance of convolutional layers.
However, there may be other groups of weights that may be tied together during the learning process, thus further re- ducing the complexity of the network.
In this paper, we adopt a recently proposed sparsity-inducing regularizer, named GrOWL (group ordered weighted l1), which encourages sparsity and, simulta- neously, learns which groups of parameters should share a common value.
GrOWL has been proven effective in linear regression, being able to identify and cope with strongly correlated covariates.
Unlike standard sparsity-inducing regularizers (e.g., l1 a.k.a.
Lasso), GrOWL not only eliminates unimportant neurons by setting all the corresponding weights to zero, but also explicitly identifies strongly correlated neurons by tying the corresponding weights to a common value.
This ability of GrOWL motivates the following two-stage procedure: (i) use GrOWL regularization in the training process to simultaneously identify significant neurons and groups of parameter that should be tied together; (ii) retrain the network, enforcing the structure that was unveiled in the previous phase, i.e., keeping only the significant neurons and enforcing the learned tying structure.
We evaluate the proposed approach on several benchmark datasets, showing that it can dramatically compress the network with slight or even no loss on generalization performance.

assistant: text: We have proposed using the recent GrOWL regularizer for simultaneous parameter sparsity and tying in DNN learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature Map Variational Auto-Encoders

Paper content:
There have been multiple attempts with variational auto-encoders (VAE) to learn powerful global representations of complex data using a combination of latent stochastic variables and an autoregressive model over the dimensions of the data.
However, for the most challenging natural image tasks the purely autoregressive model with stochastic variables still outperform the combined stochastic autoregressive models.
In this paper, we present simple additions to the VAE framework that generalize to natural images by embedding spatial information in the stochastic layers.
We significantly improve the state-of-the-art results on MNIST, OMNIGLOT, CIFAR10 and ImageNet when the feature map parameterization of the stochastic variables are combined with the autoregressive PixelCNN approach.
Interestingly, we also observe close to state-of-the-art results without the autoregressive part.
This opens the possibility for high quality image generation with only one forward-pass.

assistant: text: We present a generative model that proves state-of-the-art results on gray-scale and natural images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models

Paper content:
A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network.
Likelihood-based training  of  these  models  requires  restricting  their  architectures  to  allow  cheap computation of Jacobian determinants.
Alternatively, the Jacobian trace can be used if the transformation is specified by an ordinary differential equation.
In this paper, we use Hutchinson’s trace estimator to give a scalable unbiased estimate of the log-density.
The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.
We demonstrate our approach on high-dimensional density  estimation,  image  generation,  and  variational  inference,  achieving  the state-of-the-art among exact likelihood methods with efficient sampling.

assistant: text: We use continuous time dynamics to define a generative model with exact likelihoods and efficient sampling that is parameterized by unrestricted neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explaining Image Classifiers by Counterfactual Generation

Paper content:
When an image classifier makes a prediction, which parts of the image are relevant and why?
We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision?
Producing an answer requires marginalizing over images that could have been seen but weren't.
We can sample plausible image in-fills by conditioning a generative model on the rest of the image.
We then optimize to find the image regions that most change the classifier's decision after in-fill.
Our approach contrasts with ad-hoc in-filling approaches, such as blurring or injecting noise, which generate inputs far from the data distribution, and ignore informative relationships between different parts of the image.
Our method produces more compact and relevant saliency maps, with fewer artifacts compared to previous methods.

assistant: text: We compute saliency by using a strong generative model to efficiently marginalize over plausible alternative inputs, revealing concentrated pixel areas that preserve label information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Paper content:
Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data.
Proving tight generalization error bounds is a central question in statistical learning  theory.
In  this  paper,  we  obtain  generalization  error  bounds  for  learning general  non-convex  objectives,  which  has  attracted  significant  attention  in  recent years.
We develop a new framework,  termed Bayes-Stability,  for proving algorithm-dependent generalization error bounds.
The new framework combines ideas from both the PAC-Bayesian theory and the notion of algorithmic stability.
Applying the Bayes-Stability method, we obtain new data-dependent generalization bounds for stochastic gradient Langevin dynamics (SGLD) and several other noisy gradient methods (e.g., with momentum, mini-batch and acceleration, Entropy-SGD).
Our result recovers (and is typically tighter than) a recent result in Mou et al. (2018) and improves upon the results in Pensia et al. (2018).
Our experiments demonstrate that our data-dependent bounds can distinguish randomly labelled data from normal data, which provides an explanation to the intriguing phenomena observed in Zhang et al. (2017a).
We also study the setting where the total loss is the sum of a bounded loss and an additiona l`2 regularization term.
We obtain new generalization bounds for the continuous Langevin dynamic in this setting by developing a new Log-Sobolev inequality for the parameter distribution at any time.
Our new bounds are more desirable when the noise level of the processis not very small, and do not become vacuous even when T tends to infinity.

assistant: text: We give some generalization error bounds of noisy gradient methods such as SGLD, Langevin dynamics, noisy momentum and so forth.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

Paper content:
This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks where other state of the art methods (both with and without demonstrations) fail to see even a single successful trajectory after tens of billions of steps of exploration.

assistant: text: We introduce R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning pronunciation from a foreign language in speech synthesis networks

Paper content:
Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages.
When people learn a foreign language, their pronunciation often reflect their native language's characteristics.
That motivates us to investigate how the speech synthesis network learns the pronunciation when multi-lingual dataset is given.
In this study, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages.
Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages.
Based on the result, we also show that it is possible to train networks that synthesize English speaker's Korean speech and vice versa.
In another experiment, we train the network with limited amount of English dataset and large Korean dataset, and analyze the required amount of dataset to train a resource-poor language with the help of resource-rich languages.

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

Title: Adversarial Dropout Regularization

Paper content:
We present a domain adaptation method for transferring neural representations from label-rich source domains to unlabeled target domains.
Recent adversarial methods proposed for this task learn to align features across domains by ``fooling'' a special domain classifier network.
However, a drawback of this approach is that the domain classifier simply labels the generated features as in-domain or not, without considering the boundaries between classes.
This means that ambiguous target features can be generated near class boundaries, reducing target classification accuracy.
We propose a novel approach, Adversarial Dropout Regularization (ADR), which encourages the generator to output more discriminative features for the target domain.
Our key idea is to replace the traditional domain critic with a critic that detects non-discriminative features by using dropout on the classifier network.
The generator then learns to avoid these areas of the feature space and thus creates better features.
We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvements over the state of the art.

assistant: text: We present a new adversarial method for adapting neural representations based on a critic that detects non-discriminative features.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Skip-Thought GAN: Generating Text through Adversarial Training using Skip-Thought Vectors

Paper content:
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs).
GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer.
In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data.
Attempts have been made for utilizing GANs with word embeddings for text generation.
This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures.
The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

assistant: text: Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How to train your MAML

Paper content:
The field of few-shot learning has recently seen substantial advancements.
Most of these advancements came from casting few-shot learning as a meta-learning problem.Model Agnostic Meta Learning or MAML is currently one of the best approaches for few-shot learning via meta-learning.
MAML is simple, elegant and very powerful, however, it has a variety of issues, such as being very sensitive to neural network architectures, often leading to instability during training, requiring arduous hyperparameter searches to stabilize training and achieve high generalization and being very computationally expensive at both training and inference times.
In this paper, we propose various modifications to MAML that not only stabilize the system, but also substantially improve the generalization performance, convergence speed and computational overhead of MAML, which we call MAML++.

assistant: text: MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Evolutionary Strategies with Generative Neural Networks

Paper content:
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions.
This paper investigates the potential benefits of using highly flexible search distributions in ES algorithms, in contrast to standard ones (typically Gaussians).
We model such distributions with Generative Neural Networks (GNNs) and introduce a new ES algorithm that leverages their expressiveness to accelerate the stochastic search.
Because it acts as a plug-in, our approach allows to augment virtually any standard ES algorithm with flexible search distributions.
We demonstrate the empirical advantages of this method on a diversity of objective functions.

assistant: text: We propose a new algorithm leveraging the expressiveness of Generative Neural Networks to improve Evolutionary Strategies algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Clustering with Deep Learning: Taxonomy and New Methods

Paper content:
Clustering is a fundamental machine learning method.
The quality of its results is dependent on the data distribution.
For this reason, deep neural networks can be used for learning better representations of the data.
In this paper, we propose a systematic taxonomy for clustering with deep learning, in addition to a review of methods from the field.
Based on our taxonomy, creating new methods is more straightforward.
We also propose a new approach which is built on the taxonomy and surpasses some of the limitations of some previous work.
Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality, and performs better in some cases.

assistant: text: Unifying framework to perform clustering using deep neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Ingredients of Real World Robotic Reinforcement Learning

Paper content:
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.
In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system.
Subsequently, we investigate a number of challenges of learning without instrumentation -- including the lack of episodic resets, state estimation, and hand-engineered rewards -- and propose simple, scalable solutions to these challenges.
We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.

assistant: text: System to learn robotic tasks in the real world with reinforcement learning without instrumentation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization

Paper content:
Vanishing and exploding gradients are two of the main obstacles in training deep neural networks, especially in capturing long range dependencies in recurrent neural networks (RNNs).
In this paper, we present an efficient parametrization of the transition matrix of an RNN that allows us to stabilize the gradients that arise in its training.
Specifically, we parameterize the transition matrix by its singular value decomposition (SVD), which allows us to explicitly track and control its singular values.
We attain efficiency by using tools that are common in numerical linear algebra, namely Householder reflectors for representing the orthogonal matrices that arise in the SVD.
By explicitly controlling the singular values, our proposed svdRNN method allows us to easily solve the exploding gradient problem and we observe that it empirically solves the vanishing gradient issue to a large extent.
We note that the SVD parameterization can be used for any rectangular weight matrix, hence it can be easily extended to any deep neural network, such as a multi-layer perceptron.
Theoretically, we demonstrate that our parameterization does not lose any expressive power, and show how it potentially makes the optimization process easier.
Our extensive  experimental results also demonstrate that the proposed framework converges faster, and has good generalization, especially when the depth is large.

assistant: text: To solve the gradient vanishing/exploding problems, we proprose an efficient parametrization of the transition matrix of RNN that loses no expressive power, converges faster and has good generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Diffusion Autoencoders with Random Walk Sampling

Paper content:
Variational inference (VI) methods and especially variational autoencoders (VAEs) specify scalable generative models that enjoy an intuitive connection to manifold learning --- with many default priors the posterior/likelihood pair $q(z|x)$/$p(x|z)$ can be viewed as an approximate homeomorphism (and its inverse) between the data manifold and a latent Euclidean space.
However, these approximations are well-documented to become degenerate in training.
Unless the subjective prior is carefully chosen, the topologies of the prior and data distributions often will not match.
Conversely, diffusion maps (DM) automatically \textit{infer} the data topology and enjoy a rigorous connection to manifold learning, but do not scale easily or provide the inverse homeomorphism.
In this paper, we propose \textbf{a)} a principled measure for recognizing the mismatch between data and latent distributions and \textbf{b)} a method that combines the advantages of variational inference and diffusion maps to learn a homeomorphic generative model.
The measure, the \textit{locally bi-Lipschitz property}, is a sufficient condition for a homeomorphism and easy to compute and interpret.
The method, the \textit{variational diffusion autoencoder} (VDAE), is a novel generative algorithm that first infers the topology of the data distribution, then models a diffusion random walk over the data.
To achieve efficient computation in VDAEs, we use stochastic versions of both variational inference and manifold learning optimization.
We prove approximation theoretic results for the dimension dependence of VDAEs, and that locally isotropic sampling in the latent space results in a random walk over the reconstructed manifold.
Finally, we demonstrate the utility of our method on various real and synthetic datasets, and show that it exhibits performance superior to other generative models.

assistant: text: We combine variational inference and manifold learning (specifically VAEs and diffusion maps) to build a generative model based on a diffusion random walk on a data manifold; we generate samples by drawing from the walk's stationary distribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds

Paper content:
Point clouds, as a form of Lagrangian representation, allow for powerful and flexible applications in a large number of computational disciplines.
We propose a novel deep-learning method to learn stable and temporally coherent feature spaces for points clouds that change over time.
We identify a set of inherent problems with these approaches: without knowledge of the time dimension, the inferred solutions can exhibit strong flickering, and easy solutions to suppress this flickering can result in undesirable local minima that manifest themselves as halo structures.
We propose a novel temporal loss function that takes into account higher time derivatives of the point positions, and encourages mingling, i.e., to prevent the aforementioned halos.
We combine these techniques in a super-resolution method with a truncation approach to flexibly adapt the size of the generated positions.
We show that our method works for large, deforming point sets from different sources to demonstrate the flexibility of our approach.

assistant: text: We propose a generative neural network approach for temporally coherent point clouds.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unifying Graph Convolutional Networks as Matrix Factorization

Paper content:
In recent years, substantial progress has been made on graph convolutional networks (GCN).
In this paper, for the first time, we theoretically analyze the connections between GCN and matrix factorization (MF), and unify GCN as matrix factorization with co-training and unitization.
Moreover, under the guidance of this theoretical analysis, we propose an alternative model to GCN named Co-training and Unitized Matrix Factorization (CUMF).
The correctness of our analysis is verified by thorough experiments.
The experimental results show that CUMF achieves similar or superior performances compared to GCN.
In addition, CUMF inherits the benefits of MF-based methods to naturally support constructing mini-batches, and is more friendly to distributed computing comparing with GCN.
The distributed CUMF on semi-supervised node classification significantly outperforms distributed GCN methods.
Thus, CUMF greatly benefits large scale and complex real-world applications.

assistant: text: We unify graph convolutional networks as co-training and unitized matrix factorization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learned in Speech Recognition: Contextual Acoustic Word Embeddings

Paper content:
End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not require a lexicon.
In addition, word models may also be easier to integrate with downstream tasks such as spoken language understanding, because inference (search) is much simplified compared to phoneme, character or any other sort of sub-word units.
In this paper, we describe methods to construct contextual acoustic word embeddings directly from a supervised sequence-to-sequence acoustic-to-word speech recognition model using the learned attention distribution.
On a suite of 16 standard sentence evaluation tasks, our embeddings show competitive performance against a word2vec model trained on the speech transcriptions.
In addition, we evaluate these embeddings on a spoken language understanding task and observe that our embeddings match the performance of text-based embeddings in a pipeline of first performing speech recognition and then constructing word embeddings from transcriptions.

assistant: text: Methods to learn contextual acoustic word embeddings from an end-to-end speech recognition model that perform competitively with text-based word embeddings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Invariants through Soft Unification

Paper content:
Human reasoning involves recognising common underlying principles across many examples by utilising variables.
The by-products of such reasoning are invariants that capture patterns across examples such as "if someone went somewhere then they are there" without mentioning specific people or places.
Humans learn what variables are and how to use them at a young age, and the question this paper addresses is whether machines can also learn and use variables solely from examples without requiring human pre-engineering.
We propose Unification Networks that incorporate soft unification into neural networks to learn variables and by doing so lift examples into invariants that can then be used to solve a given task.
We evaluate our approach on four datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.

assistant: text: End-to-end learning of invariant representations with variables across examples such as if someone went somewhere then they are there.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rate and Global Landscape Analysis

Paper content:
We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector
$\theta_0\in\mathbb{R}^d$ \emph{uniformly} from $m$ quantized noisy measurements.
Under the assumption that the measurements are sub-Gaussian,  to recover any $k$-sparse $\theta_0$ ($k\ll d$) \emph{uniformly} up to an error $\varepsilon$ with high probability, the best known computationally tractable algorithm requires\footnote{Here, an algorithm is ``computationally tractable'' if it has provable convergence guarantees.
The notation $\tilde{\mathcal{O}}(\cdot)$ omits a logarithm factor of $\varepsilon^{-1}$.
} $m\geq\tilde{\mathcal{O}}(k\log d/\varepsilon^4)$.
In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$.
Such a framework poses low-dimensional priors on $\theta_0$ without a known basis.
We propose to recover the target $G(x_0)$ via an unconstrained empirical risk minimization (ERM) problem under a much weaker \emph{sub-exponential  measurement assumption}.
For such a problem, we establish a joint statistical and computational analysis
. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of $m=\tilde{\mathcal{O}} (kn\log d /\epsilon^2)$ recovering any $G(x_0)$ uniformly up to an error $\varepsilon$.
Moreover, from the lens of computation, we prove that under proper conditions on the ReLU weights, our proposed empirical risk, despite non-convexity, has no stationary point outside of small neighborhoods around the true representation $x_0$ and its negative multiple.
Furthermore, we show that the global minimizer of the empirical risk stays within the neighborhood around $x_0$ rather than its negative multiple.
Our analysis sheds some light on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.

assistant: text: We provide statistical and computational analysis of one-bit compressed sensing problem with a generative prior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Differentiable Hebbian Plasticity for Continual Learning

Paper content:
Catastrophic forgetting poses a grand challenge for continual learning systems, which prevents neural networks from protecting old knowledge while learning new tasks sequentially.
We propose a Differentiable Hebbian Plasticity (DHP) Softmax layer which adds a fast learning plastic component to the slow weights of the softmax output layer.
The DHP Softmax behaves as a compressed episodic memory that reactivates existing memory traces, while creating new ones.
We demonstrate the flexibility of our model by combining it with existing well-known consolidation methods to prevent catastrophic forgetting.
We evaluate our approach on the Permuted MNIST and Split MNIST benchmarks, and introduce Imbalanced Permuted MNIST — a dataset that combines the challenges of class imbalance and concept drift.
Our model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.

assistant: text: Hebbian plastic weights can behave as a compressed episodic memory storage in neural networks; improving their ability to alleviate catastrophic forgetting in continual learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Training a Constrained Natural Media Painting Agent using Reinforcement Learning 

Paper content:
We present a novel approach to train a natural media painting using reinforcement learning.
Given a reference image,  our formulation is based on stroke-based rendering that  imitates human drawing and can be learned from scratch without supervision.
Our painting agent computes a sequence of actions that represent the primitive painting strokes.
In order to ensure that the generated policy is predictable and controllable, we use a constrained learning method and train the painting agent  using the environment model and follows the commands encoded in an observation.
We have applied our approach on many benchmarks and our results demonstrate that our constrained agent can handle different painting media and different constraints in the action space to collaborate with humans or other agents.

assistant: text: We train a natural media painting agent using environment model. Based on our painting agent, we present a novel approach to train a constrained painting agent that follows the command encoded in the observation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack

Paper content:
There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations.
The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level.
The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause misclassification, also known as the margin of an input feature.
While the former paradigm is well-resolved, the latter is not.
Existing zero-confidence attacks either introduce significant approximation errors, or are too time-consuming.
We therefore propose MarginAttack, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency.
Our experiments show that MarginAttack is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation attacks.
In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.

assistant: text: This paper introduces MarginAttack, a stronger and faster zero-confidence adversarial attack.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete-Valued Neural Networks Using Variational Inference

Paper content:
The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs.
While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs.
To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions.
In our experiments, we show that our model achieves state of the art performance on several real world data sets.
In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.

assistant: text: Variational Inference for infering a discrete distribution from which a low-precision neural network is derived