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

Title: REPRESENTATION COMPRESSION AND GENERALIZATION IN DEEP NEURAL NETWORKS

Paper content:
Understanding the groundbreaking performance of Deep Neural Networks is one
of the greatest challenges to the scientific community today.
In this work, we
introduce an information theoretic viewpoint on the behavior of deep networks
optimization processes and their generalization abilities.
By studying the Information
Plane, the plane of the mutual information between the input variable and
the desired label, for each hidden layer.
Specifically, we show that the training of
the network is characterized by a rapid increase in the mutual information (MI)
between the layers and the target label, followed by a longer decrease in the MI
between the layers and the input variable.
Further, we explicitly show that these
two fundamental information-theoretic quantities correspond to the generalization
error of the network, as a result of introducing a new generalization bound that is
exponential in the representation compression.
The analysis focuses on typical
patterns of large-scale problems.
For this purpose, we introduce a novel analytic
bound on the mutual information between consecutive layers in the network.
An important consequence of our analysis is a super-linear boost in training time
with the number of non-degenerate hidden layers, demonstrating the computational
benefit of the hidden layers.

assistant: text: Introduce an information theoretic viewpoint on the behavior of deep networks optimization processes and their generalization abilities
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A2BCD: Asynchronous Acceleration with Optimal Complexity

Paper content:
In this paper, we propose the Asynchronous Accelerated Nonuniform Randomized Block Coordinate Descent algorithm (A2BCD).
We prove A2BCD converges linearly to a solution of the convex minimization problem at the same rate as NU_ACDM, so long as the maximum delay is not too large.
This is the first asynchronous Nesterov-accelerated algorithm that attains any provable speedup.
Moreover, we then prove that these algorithms both have optimal complexity.
Asynchronous algorithms complete much faster iterations, and A2BCD has optimal complexity.
Hence we observe in experiments that A2BCD is the top-performing coordinate descent algorithm, converging up to 4-5x faster than NU_ACDM on some data sets in terms of wall-clock time.
To motivate our theory and proof techniques, we also derive and analyze a continuous-time analog of our algorithm and prove it converges at the same rate.

assistant: text: We prove the first-ever convergence proof of an asynchronous accelerated algorithm that attains a speedup.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

Paper content:
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework.
The first problem is implicit bias present in the reward functions used in these algorithms.
While these biases might work well for some environments, they can also lead to sub-optimal behavior in others.
Secondly, even though these algorithms can learn from few expert demonstrations, they require a prohibitively large number of interactions with the environment in order to imitate the expert for many real-world applications.
In order to address these issues, we propose a new algorithm called Discriminator-Actor-Critic that uses off-policy Reinforcement Learning to reduce policy-environment interaction sample complexity by an average factor of 10.
Furthermore, since our reward function is designed to be unbiased, we can apply our algorithm to many problems without making any task-specific adjustments.

assistant: text: We address sample inefficiency and reward bias in adversarial imitation learning algorithms such as GAIL and AIRL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation

Paper content:
Recent advances in Generative Adversarial Networks (GANs) – in architectural design,  training strategies,  and empirical tricks – have led nearly photorealistic samples on large-scale datasets such as ImageNet.
In fact, for one model in particular, BigGAN, metrics such as Inception Score or Frechet Inception Distance nearly match those of the dataset, suggesting that these models are close to match-ing the distribution of the training set.
Given the quality of these models,  it is worth understanding to what extent these samples can be used for data augmentation, a task expressed as a long-term goal of the GAN research project.
To that end, we train ResNet-50 classifiers using either purely BigGAN images or mixtures of ImageNet and BigGAN images, and test on the ImageNet validation set.
Our  preliminary  results  suggest  both a measured view of  state-of-the-art  GAN quality and highlight limitations of current metrics.
Using only BigGAN images, we find that Top-1 and Top-5 error increased by 120% and 384%, respectively, and furthermore, adding more BigGAN data to the ImageNet training set at best only marginally improves classifier performance.
Finally, we find that neither Inception Score, nor FID, nor combinations thereof are predictive of classification accuracy.
These results suggest that as GANs are beginning to be deployed in downstream tasks, we should create metrics that better measure downstream task performance.
We propose classification performance as one such metric that, in addition to assessing per-class sample quality, is more suited to such downstream tasks.

assistant: text: BigGANs do not capture the ImageNet data distributions and are only modestly successful for data augmentation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The (Un)reliability of saliency methods

Paper content:
Saliency methods aim to explain the predictions of deep neural networks.
These methods lack reliability when the explanation is sensitive to factors that do not contribute to the model prediction.
We use a simple and common pre-processing step ---adding a mean shift to the input data--- to show that a transformation with no effect on the model can cause numerous methods to incorrectly attribute.
We define input invariance as the requirement that a saliency method mirror the sensitivity of the model with respect to transformations of the input.
We show, through several examples, that saliency methods that do not satisfy a input invariance property are unreliable and can lead to misleading and inaccurate attribution.

assistant: text: Attribution can sometimes be misleading
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples

Paper content:
Hierarchical Bayesian methods have the potential to unify many related tasks (e.g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.
We show that existing approaches for learning such models can fail on expressive generative networks such as PixelCNNs, by describing the global distribution with little reliance on latent variables.
To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class; the result, which we call a Variational Homoencoder (VHE), may be understood as training a hierarchical latent variable model which better utilises latent variables in these cases.
Using this framework enables us to train a hierarchical PixelCNN for the Omniglot dataset, outperforming all existing models on test set likelihood.
With a single model we achieve both strong one-shot generation and near human-level classification, competitive with state-of-the-art discriminative classifiers.
The VHE objective extends naturally to richer dataset structures such as factorial or hierarchical categories, as we illustrate by training models to separate character content from simple variations in drawing style, and to generalise the style of an alphabet to new characters.

assistant: text: Technique for learning deep generative models with shared latent variables, applied to Omniglot with a PixelCNN decoder.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Transformer to CNN: Label-scarce distillation for efficient text classification

Paper content:
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018.
The new approaches allow for accurate results, even when there is little labelled data, because these NLP models can benefit from training on both task-agnostic and task-specific unlabelled data.
However, these advantages come with significant size and computational costs.
This workshop paper outlines how our proposed convolutional student architecture, having been trained by a distillation process from a large-scale model, can achieve 300x inference speedup and 39x reduction in parameter count.
In some cases, the student model performance surpasses its teacher on the studied tasks.

assistant: text: We train a small, efficient CNN with the same performance as the OpenAI Transformer on text classification tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Latent Question Reformulation and Information Accumulation for Multi-Hop Machine Reading

Paper content:
Multi-hop text-based question-answering is a current challenge in machine comprehension.
This task requires to sequentially integrate facts from multiple passages to answer complex natural language questions.
In this paper, we propose a novel architecture, called the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.
LQR-net is composed of an association of \textbf{reading modules} and \textbf{reformulation modules}.
The purpose of the reading module is to produce a question-aware representation of the document.
From this document representation, the reformulation module extracts essential elements to calculate an updated representation of the question.
This updated question is then passed to the following hop.
We evaluate our architecture on the \hotpotqa question-answering dataset designed to assess multi-hop reasoning capabilities.
Our model achieves competitive results on the public leaderboard and outperforms the best current \textit{published} models in terms of Exact Match (EM) and $F_1$ score.
Finally, we show that an analysis of the sequential reformulations can provide interpretable reasoning paths.

assistant: text: In this paper, we propose the Latent Question Reformulation Network (LQR-net), a multi-hop and parallel attentive network designed for question-answering tasks that require reasoning capabilities.
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 a novel method for incorporating conditional information into a generative adversarial network (GAN) for structured prediction tasks.
This method is based on fusing features from the generated and conditional information in feature space and allows the discriminator to better capture higher-order statistics from the data.
This method also increases the strength of the signals passed through the network where the real or generated data and the conditional data agree.
The proposed method is conceptually simpler than the joint convolutional neural network - conditional Markov random field (CNN-CRF) models and enforces higher-order consistency without being limited to a very specific class of high-order potentials.
Experimental results demonstrate that this method leads to improvement on a variety of different structured prediction tasks including image synthesis, semantic segmentation, and depth estimation.

assistant: text: We propose a novel way to incorporate conditional image information into the discriminator of GANs using feature fusion that can be used for structured prediction tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Representations of Sets through Optimized Permutations

Paper content:
Representations of sets are challenging to learn because operations on sets should be permutation-invariant.
To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end.
The permuted set can be further processed to learn a permutation-invariant representation of that set, avoiding a bottleneck in traditional set models.
We demonstrate our model's ability to learn permutations and set representations with either explicit or implicit supervision on four datasets, on which we achieve state-of-the-art results: number sorting, image mosaics, classification from image mosaics, and visual question answering.

assistant: text: Learn how to permute a set, then encode permuted set with RNN to obtain a set representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

Paper content:
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success.
This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient exploration for learning both individual goal attainment and cooperation for others' success, and credit-assignment for interactions between actions and goals of different agents.
To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
We use a function augmentation scheme to bridge value and policy functions across the curriculum.
The complete architecture, called CM3, learns significantly faster than direct adaptations of existing algorithms on three challenging multi-goal multi-agent problems: cooperative navigation in difficult formations, negotiating multi-vehicle lane changes in the SUMO traffic simulator, and strategic cooperation in a Checkers environment.

assistant: text: A modular method for fully cooperative multi-goal multi-agent reinforcement learning, based on curriculum learning for efficient exploration and credit assignment for action-goal interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Feature Matching Networks

Paper content:
We propose a non-adversarial feature matching-based approach to train generative models.
Our approach, Generative Feature Matching Networks (GFMN), leverages pretrained neural networks such as autoencoders and ConvNet classifiers to perform feature extraction.
We perform an extensive number of experiments with different challenging datasets, including ImageNet.
Our experimental results demonstrate that, due to the expressiveness of the features from pretrained ImageNet classifiers, even by just matching first order statistics, our approach can achieve state-of-the-art results for challenging benchmarks such as CIFAR10 and STL10.

assistant: text: A new non-adversarial feature matching-based approach to train generative models that achieves state-of-the-art results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers

Paper content:
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly ﬁnd the optimal network parameters and sparse network structure in a uniﬁed optimization process with trainable pruning thresholds.
These thresholds can have ﬁne-grained layer-wise adjustments dynamically via backpropagation.
We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same training epochs as dense models.
Dynamic Sparse Training achieves prior art performance compared with other sparse training algorithms on various network architectures.
Additionally, we have several surprising observations that provide strong evidence to the effectiveness and efﬁciency of our algorithm.
These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures.

assistant: text: We present a novel network pruning method that can find the optimal sparse structure during the training process with trainable pruning threshold
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Graph: Few shot Link Prediction via Meta Learning

Paper content:
We consider the task of few shot link prediction, where the goal is to predict missing edges across multiple graphs using only a small sample of known edges.
We show that current link prediction methods are generally ill-equipped to handle this task---as they cannot effectively transfer knowledge between graphs in a multi-graph setting and are unable to effectively learn from very sparse data.
To address this challenge, we introduce a new gradient-based meta learning framework, Meta-Graph, that leverages higher-order gradients along with a learned graph signature function that conditionally generates a graph neural network initialization.
Using a novel set of few shot link prediction benchmarks, we show that Meta-Graph enables not only fast adaptation but also better final convergence and can effectively learn using only a small sample of true edges.

assistant: text: We apply gradient based meta-learning to the graph domain and introduce a new graph specific transfer function to further bootstrap the process.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers

Paper content:
We consider the problem of uncertainty estimation in the context of (non-Bayesian) deep neural classification.
In this context, all known methods are based on extracting uncertainty signals from a trained network optimized to solve the classification problem at hand.
We demonstrate that such techniques tend to introduce biased estimates for instances whose predictions are supposed to be highly confident.
We argue that this deficiency is an artifact of the dynamics of training with SGD-like optimizers, and it has some properties similar to overfitting.
Based on this observation, we develop an uncertainty estimation algorithm that selectively estimates the uncertainty of highly confident points, using earlier snapshots of the trained model, before their estimates are jittered (and way before they are ready for actual classification).
We present extensive experiments indicating that the proposed algorithm provides uncertainty estimates that are consistently better than all known methods.

assistant: text: We use snapshots from the training process to improve any uncertainty estimation method of a DNN classifier.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title:  Large-Scale Visual Speech Recognition

Paper content:
This work presents a scalable solution to continuous visual speech recognition.
To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3,886 hours of video).
In tandem, we designed and trained an integrated lipreading system, consisting of a video processing pipeline that maps raw video to stable videos of lips and sequences of phonemes, a scalable deep neural network that maps the lip videos to sequences of phoneme distributions, and a production-level speech decoder that outputs sequences of words.
The proposed system achieves a word error rate (WER) of 40.9% as measured on a held-out set.
In comparison, professional lipreaders achieve either 86.4% or 92.9% WER on the same dataset when having access to additional types of contextual information.
Our approach significantly improves on previous lipreading approaches, including variants of LipNet and of Watch, Attend, and Spell (WAS), which are only capable of 89.8% and 76.8% WER respectively.

assistant: text: This work presents a scalable solution to continuous visual speech recognition.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DCN+: Mixed Objective And Deep Residual Coattention for Question Answering

Paper content:
Traditional models for question answering optimize using cross entropy loss, which encourages exact answers at the cost of penalizing nearby or overlapping answers that are sometimes equally accurate.
We propose a mixed objective that combines cross entropy loss with self-critical policy learning, using rewards derived from word overlap to solve the misalignment between evaluation metric and optimization objective.
In addition to the mixed objective, we introduce a deep residual coattention encoder that is inspired by recent work in deep self-attention and residual networks.
Our proposals improve model performance across question types and input lengths, especially for long questions that requires the ability to capture long-term dependencies.
On the Stanford Question Answering Dataset, our model achieves state of the art results with 75.1% exact match accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy and 86.0% F1.

assistant: text: We introduce the DCN+ with deep residual coattention and mixed-objective RL, which achieves state of the art performance on the Stanford Question Answering Dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Probabilistic Motor Primitives for Humanoid Control

Paper content:
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids.
To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck.
We show that it is possible to train this model entirely offline to compress thousands of expert policies and learn a motor primitive embedding space.
The trained neural probabilistic motor primitive system can perform one-shot imitation of whole-body humanoid behaviors, robustly mimicking unseen trajectories.
Additionally, we demonstrate that it is also straightforward to train controllers to reuse the learned motor primitive space to solve tasks, and the resulting movements are relatively naturalistic.
To support the training of our model, we compare two approaches for offline policy cloning, including an experience efficient method which we call linear feedback policy cloning.
We encourage readers to view a supplementary video (https://youtu.be/CaDEf-QcKwA ) summarizing our results.

assistant: text: Neural Probabilistic Motor Primitives compress motion capture tracking policies into one flexible model capable of one-shot imitation and reuse as a low-level controller.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding Posterior Collapse in Generative Latent Variable Models

Paper content:
Posterior collapse in Variational Autoencoders (VAEs) arises when the variational distribution closely matches the uninformative prior for a subset of latent variables.
This paper presents a simple and intuitive explanation for posterior collapse through the analysis of linear VAEs and their direct correspondence with Probabilistic PCA (pPCA).
We identify how local maxima can emerge from the marginal log-likelihood of pPCA, which yields similar local maxima for the evidence lower bound (ELBO).
We show that training a linear VAE with variational inference recovers a uniquely identifiable global maximum corresponding to the principal component directions.
We provide empirical evidence that the presence of local maxima causes posterior collapse in deep non-linear VAEs.
Our findings help to explain a wide range of heuristic approaches in the literature that attempt to diminish the effect of the KL term in the ELBO to reduce posterior collapse.

assistant: text: We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data

Paper content:
As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can.
As a result, machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program.
At the same time, data owners would like to trade their data for its value, without having to first give away the data itself be- fore receiving compensation.
It is difficult for data providers and model providers to agree on a fair price without first revealing the data or the trained model to the other side.
Escrow systems only complicate this further, adding an additional layer of trust required of both parties.
Currently, data owners and model owners don’t have a fair pricing system that eliminates the need to trust a third party and training the model on the data, which
1) takes a long time to complete,
2) does not guarantee that useful data is paid valuably and that useless data isn’t, without trusting in the third party with both the model and the data.
Existing improve- ments to secure the transaction focus heavily on encrypting or approximating the data, such as training on encrypted data, and variants of federated learning.
As powerful as the methods appear to be, we show them to be impractical in our use case with real world assumptions for preserving privacy for the data owners when facing black-box models.
Thus, a fair pricing scheme that does not rely on secure data encryption and obfuscation is needed before the exchange of data.
This pa- per proposes a novel method for fair pricing using data-model efficacy techniques such as influence functions, model extraction, and model compression methods, thus enabling secure data transactions.
We successfully show that without running the data through the model, one can approximate the value of the data; that is, if the data turns out redundant, the pricing is minimal, and if the data leads to proper improvement, its value is properly assessed, without placing strong assumptions on the nature of the model.
Future work will be focused on establishing a system with stronger transactional security against adversarial attacks that will reveal details about the model or the data to the other party.

assistant: text: Facing complex, black-box models, encrypting the data is not as usable as approximating the model and using it to price a potential transaction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation

Paper content:
Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks.
However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods.
These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal.
As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance.
This solution dubbed CHAMELEON leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain knowledge inspired logic to improve the samples itself.
Experimentation with real hardware shows that CHAMELEON provides 4.45×speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.

assistant: text: Reinforcement learning and Adaptive Sampling for Optimized Compilation of Deep Neural Networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Image Classification Through Top-Down Image Pyramid Traversal

Paper content:
The available resolution in our visual world is extremely high, if not infinite.
Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information.
In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are.
We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts.
We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest.
Gains in performance come for less FLOPs, because of the selective processing that we follow.
Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.

assistant: text: We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Stable and comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training

Paper content:
Domain adaptation tackles the problem of transferring knowledge from a label-rich source domain to an unlabeled or label-scarce target domain.
Recently domain-adversarial training (DAT) has shown promising capacity to learn a domain-invariant feature space by reversing the gradient propagation of a domain classifier.
However, DAT is still vulnerable in several aspects including (1) training instability due to the overwhelming discriminative ability of the domain classifier in adversarial training, (2) restrictive feature-level alignment, and (3) lack of interpretability or systematic explanation of the learned feature space.
In this paper, we propose a novel Max-margin Domain-Adversarial Training (MDAT) by designing an Adversarial Reconstruction Network (ARN).
The proposed MDAT stabilizes the gradient reversing in ARN by replacing the domain classifier with a reconstruction network, and in this manner ARN conducts both feature-level and pixel-level domain alignment without involving extra network structures.
Furthermore, ARN demonstrates strong robustness to a wide range of hyper-parameters settings, greatly alleviating the task of model selection.
Extensive empirical results validate that our approach outperforms other state-of-the-art domain alignment methods.
Additionally, the reconstructed target samples are visualized to interpret the domain-invariant feature space which conforms with our intuition.

assistant: text: A stable domain-adversarial training approach for robust and comprehensive domain adaptation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards

Paper content:
Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics.
Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated actions, it can drift away from demonstrated states due to error accumulation.
Recent methods based on reinforcement learning (RL), such as inverse RL and generative adversarial imitation learning (GAIL), overcome this issue by training an RL agent to match the demonstrations over a long horizon.
Since the true reward function for the task is unknown, these methods learn a reward function from the demonstrations, often using complex and brittle approximation techniques that involve adversarial training.
We propose a simple alternative that still uses RL, but does not require learning a reward function.
The key idea is to provide the agent with an incentive to match the demonstrations over a long horizon, by encouraging it to return to demonstrated states upon encountering new, out-of-distribution states.
We accomplish this by giving the agent a constant reward of r=+1 for matching the demonstrated action in a demonstrated state, and a constant reward of r=0 for all other behavior.
Our method, which we call soft Q imitation learning (SQIL), can be implemented with a handful of minor modifications to any standard Q-learning or off-policy actor-critic algorithm.
Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
Empirically, we show that SQIL outperforms BC and achieves competitive results compared to GAIL, on a variety of image-based and low-dimensional tasks in Box2D, Atari, and MuJoCo.
This paper is a proof of concept that illustrates how a simple imitation method based on RL with constant rewards can be as effective as more complex methods that use learned rewards.

assistant: text: A simple and effective alternative to adversarial imitation learning: initialize experience replay buffer with demonstrations, set their reward to +1, set reward for all other data to 0, run Q-learning or soft actor-critic to train.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improved Disentanglement through Learned Aggregation of Convolutional Feature Maps

Paper content:
We present and discuss a simple image preprocessing method for learning disentangled latent factors.
In particular, we utilize the implicit inductive bias contained in features from networks pretrained on the ImageNet database.
We enhance this bias by explicitly fine-tuning such pretrained networks on tasks useful for the NeurIPS2019 disentanglement challenge, such as angle and position estimation or color classification.
Furthermore, we train a VAE on regionally aggregate feature maps, and discuss its disentanglement performance using metrics proposed in recent literature.
Fully unsupervised methods, that is, without any human supervision, are doomed to fail for tasks such as learning disentangled representations (Locatello et al., 2018) .
In this contribution, we utilize the implicit inductive bias contained in models pretrained on the ImageNet database (Russakovsky et al., 2014) , and enhance it by finetuning such models on challenge-relevant tasks such as angle and position estimation or color classification.
In particular, our submission for challenge stage 2 builds on our submission from stage 1 1 , in which we employed pretrained CNNs to extract convolutional feature maps as a preprocessing step before training a VAE (Kingma and Welling, 2013) .
Although this approach already results in partial disentanglement, we identified two issues with the feature vectors extracted this way.
Firstly, the feature extraction network is trained on ImageNet, which is rather dissimilar to the MPI3d dataset used in the challenge.
Secondly, the feature aggregation mechanism was chosen ad-hoc and likely does not retain all information needed for disentanglement.
We attempt to fix these issues by finetuning the feature extraction network as well as learning the aggregation of feature maps from data by using the labels of the simulation datasets MPI3d-toy and MPI3d-realistic.
On the public leaderboard (i.e. on MPI3D-real ), our best submission achieves the first rank on the FactorVAE (Kim and Mnih, 2018) , and DCI (Eastwood and Williams, 2018 ) metrics, with a large gap to the second-placed entry.
See appendix A for a discussion of the results.
Unsurprisingly, introducing prior knowledge simplifies the disentanglement task considerably, reflected in improved scores.
To do so, our approach makes use of task-specific supervision obtained from simulation, which restricts its applicability.
Nevertheless, it constitutes a demonstration that this type of supervision can transfer to better disentanglement on real world data, which was one of the goals of the challenge.

assistant: text: We use supervised finetuning of feature vectors to improve transfer from simulation to the real world
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Video Generation on Complex Datasets

Paper content:
Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale.
We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work.
Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator.
We evaluate on the related tasks of video synthesis and video prediction,  and achieve new state-of-the-art Fréchet Inception Distance for prediction for Kinetics-600,  as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.

assistant: text: We propose DVD-GAN, a large video generative model that is state of the art on several tasks and produces highly complex videos when trained on large real world datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evolution of Eigenvalue Decay in Deep Networks

Paper content:
The linear transformations in converged deep networks show fast eigenvalue decay.
The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.
We use a stochastic approximator to generate histograms of eigenvalues.
This allows us to investigate layers with hundreds of thousands of dimensions.
We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.

assistant: text: We investigate the eigenvalues of the linear layers in deep networks and show that the distributions develop heavy-tail behavior during training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Differentially Private Federated Learning: A Client Level Perspective

Paper content:
Federated learning is a recent advance in privacy protection.
In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients.
The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data.
However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization.
In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model.
We tackle this problem and propose an algorithm for client sided differential privacy preserving federated optimization.
The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance.
Empirical studies suggest that given a sufficiently large number of participating clients, our proposed procedure can maintain client-level differential privacy at only a minor cost in model performance.

assistant: text: Ensuring that models learned in federated fashion do not reveal a client's participation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TSInsight: A local-global attribution framework for interpretability in time-series data

Paper content:
With the rise in employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before.
Although many different directions regarding interpretability have been explored for visual modalities, time-series data has been neglected with only a handful of methods tested due to their poor intelligibility.
We approach the problem of interpretability in a novel way by proposing TSInsight where we attach an auto-encoder with a sparsity-inducing norm on its output to the classifier and fine-tune it based on the gradients from the classifier and a reconstruction penalty.
The auto-encoder learns to preserve features that are important for the prediction by the classifier and suppresses the ones that are irrelevant i.e. serves as a feature attribution method to boost interpretability.
In other words, we ask the network to only reconstruct parts which are useful for the classifier i.e. are correlated or causal for the prediction.
In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations.
We evaluated TSInsight along with other commonly used attribution methods on a range of different time-series datasets to validate its efficacy.
Furthermore, we analyzed the set of properties that TSInsight achieves out of the box including adversarial robustness and output space contraction.
The obtained results advocate that TSInsight can be an effective tool for the interpretability of deep time-series models.

assistant: text: We present an attribution technique leveraging sparsity inducing norms to achieve interpretability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Multi Source Domain Adaptation

Paper content:
Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset.
However, in practical applications, we typically have access to multiple sources.
In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.
Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content.
For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style.
In this way, new labeled images can be generated which are used to train a final target classifier.
We test our  approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.

assistant: text: In this paper we propose generative method for multisource domain adaptation based on decomposition of content, style and domain factors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MULTI-MODAL GEOLOCATION ESTIMATION USING DEEP NEURAL NETWORKS

Paper content:
Estimating the location where an image was taken based solely on the contents of the image is a challenging task, even for humans, as properly labeling an image in such a fashion relies heavily on contextual information, and is not as simple as identifying  a single object in the image.
Thus any methods which attempt to do so must somehow account for these complexities, and no single model to date is completely capable of addressing all challenges.
This work contributes to the state of research in image geolocation inferencing by introducing a novel global meshing strategy, outlining a variety of training procedures to overcome the considerable data limitations when training these models, and demonstrating how incorporating additional information can be used to improve the overall performance of a geolocation inference model.
In this work, it is shown that Delaunay triangles are an effective type of mesh for geolocation in relatively low volume scenarios when compared to results from state of the art models which use quad trees and an order of magnitude more training data.
In addition, the time of posting, learned user albuming, and other meta data are easily incorporated to improve geolocation by up to 11% for country-level (750 km) locality accuracy to 3% for city-level (25 km) localities.

assistant: text: A global geolocation inferencing strategy with novel meshing strategy and demonstrating incorporating additional information can be used to improve the overall performance of a geolocation inference model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Domain Adaptation through Self-Supervision

Paper content:
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability.
The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously.
Each self-supervised task brings the two domains closer together along the direction relevant to that task.
Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain.
The presented objective is straightforward to implement and easy to optimize.
We achieve state-of-the-art results on four out of seven standard benchmarks, and competitive results on segmentation adaptation.
We also demonstrate that our method composes well with another popular pixel-level adaptation method.

assistant: text: We use self-supervision on both domain to align them for unsupervised domain adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

Paper content:
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task.
The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations.
Discretization enables the direct application of algorithms from the NLP community which require discrete inputs.
Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.

assistant: text: Learn how to quantize speech signal and apply algorithms requiring discrete inputs to audio data such as BERT.