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

Title: Hamiltonian Generative Networks

Paper content:
The Hamiltonian formalism plays a central role in classical and quantum physics.
Hamiltonians are the main tool for modelling the continuous time evolution of systems with conserved quantities, and they come equipped with many useful properties, like time reversibility and smooth interpolation in time.
These properties are important for many machine learning problems - from sequence prediction to reinforcement learning and density modelling - but are not typically provided out of the box by standard tools such as recurrent neural networks.
In this paper, we introduce the Hamiltonian Generative Network (HGN), the first approach capable of consistently learning Hamiltonian dynamics from high-dimensional observations (such as images) without restrictive domain assumptions.
Once trained, we can use HGN to sample new trajectories, perform rollouts both forward and backward in time, and even speed up or slow down the learned dynamics.
We demonstrate how a simple modification of the network architecture turns HGN into a powerful normalising flow model, called Neural Hamiltonian Flow (NHF), that uses Hamiltonian dynamics to model expressive densities.
Hence, we hope that our work serves as a first practical demonstration of the value that the Hamiltonian formalism can bring to machine learning.
More results and video evaluations are available at: http://tiny.cc/hgn
We have presented HGN, the first deep learning approach capable of reliably learning Hamiltonian dynamics from pixel observations.
We have evaluated our approach on four classical physical systems and demonstrated that it outperformed the only relevant baseline by a large margin.
Hamiltonian dynamics have a number of useful properties that can be exploited more widely by the machine learning community.
For example, the Hamiltonian induces a smooth manifold and a vector field in the abstract phase space along which certain physical quantities are conserved.
The time evolution along these paths is also completely reversible.
These properties can have wide implications in such areas of machine learning as reinforcement learning, representation learning and generative modelling.
We have demonstrated the first step towards applying the learnt Hamiltonian dynamics as normalising flows for expressive yet computationally efficient density modelling.
We hope that this work serves as the first step towards a wider adoption of the rich body of physics literature around the Hamiltonian principles in the machine learning community.
A SUPPLEMENTARY MATERIALS

assistant: text: We introduce a class of generative models that reliably learn Hamiltonian dynamics from high-dimensional observations. The learnt Hamiltonian can be applied to sequence modeling or as a normalising flow.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MDE: Multiple Distance Embeddings for Link Prediction in Knowledge Graphs

Paper content:
Over the past decade, knowledge graphs became popular for capturing structured domain knowledge.
Relational learning models enable the prediction of missing links inside knowledge graphs.
More specifically, latent distance approaches model the relationships among entities via a distance between latent representations.
Translating embedding models (e.g., TransE) are among the most popular latent distance approaches which use one distance function to learn multiple relation patterns.
However, they are mostly inefficient in capturing symmetric relations since the representation vector norm for all the symmetric relations becomes equal to zero.
They also lose information when learning relations with reflexive patterns since they become symmetric and transitive.
We propose the Multiple Distance Embedding model (MDE) that addresses these limitations and a framework which enables collaborative combinations of latent distance-based terms (MDE).
Our solution is based on two principles:
1) using limit-based loss instead of margin ranking loss and
2) by learning independent embedding vectors for each of terms we can collectively train and predict using contradicting distance terms.
We further demonstrate that MDE allows modeling relations with (anti)symmetry, inversion, and composition patterns.
We propose MDE as a neural network model which allows us to map non-linear relations between the embedding vectors and the expected output of the score function.
Our empirical results show that MDE outperforms the state-of-the-art embedding models on several benchmark datasets.

assistant: text: A novel method of modelling Knowledge Graphs based on Distance Embeddings and Neural Networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Inductive Transfer Learning with input and output space adaptation

Paper content:
We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains.
AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies.
Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information.
The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets.
Discrepancies exist between
1) the genomic data of pre-clinical and clinical datasets (the input space), and
2) the different measures of the drug response (the output space).
To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies.
Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.

assistant: text: A novel method of inductive transfer learning that employs adversarial learning and multi-task learning to address the discrepancy in input and output space
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Uncertainty-guided Continual Learning with Bayesian Neural Networks

Paper content:
Continual learning aims to learn new tasks without forgetting previously learned ones.
This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity.
Current regularization-based continual learning algorithms  need an external representation and extra computation to measure the parameters' \textit{importance}.
In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in  networks.
Uncertainty is a natural way to identify \textit{what to remember} and \textit{what to change} as we continually learn,  and thus mitigate catastrophic forgetting.
We also show a variant of our model, which uses uncertainty for weight pruning
and retains task performance after pruning by saving binary masks per tasks.
We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches.
Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.

assistant: text: A regularization-based approach for continual learning using Bayesian neural networks to predict parameters' importance
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neuron ranking - an informed way to compress convolutional neural networks

Paper content:
Convolutional neural networks (CNNs) in recent years have made a dramatic impact in science, technology and industry, yet the theoretical mechanism of CNN architecture design remains surprisingly vague.
The CNN neurons, including its distinctive element, convolutional filters, are known to be learnable features, yet their individual role in producing the output is rather unclear.
The thesis of this work is that not all neurons are equally important and some of them contain more useful information to perform a given task.
Hence, we propose to quantify and rank neuron importance, and directly incorporate neuron importance in the objective function under two formulations: (1) a game theoretical approach based on Shapley value which computes the marginal contribution of each filter; and (2) a probabilistic approach based on what-we-call, the importance switch using variational inference.
Using these two methods we confirm the general theory that some of the neurons are inherently more important than the others.
Various experiments illustrate that learned ranks can be readily useable for structured network compression and interpretability of learned features.

assistant: text: We propose CNN neuron ranking with two different methods and show their consistency in producing the result which allows to interpret what network deems important and compress the network by keeping the most relevant nodes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universal Approximation with Certified Networks

Paper content:
Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks.
However, it is currently very difficult to train a neural network that is both accurate and certifiably robust.
In this work we take a step towards addressing this challenge.
We prove that for every continuous function $f$, there exists a network $n$ such that:
(i) $n$ approximates $f$ arbitrarily close, and
(ii) simple interval bound propagation of a region $B$ through $n$ yields a result that is arbitrarily close to the optimal output of $f$ on $B$.
Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks.
To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.

assistant: text: We prove that for a large class of functions f there exists an interval certified robust network approximating f up to arbitrary precision.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Paper content:
Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data.
For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems.
In particular, learning physics models for model-based control requires robust extrapolation from fewer samples – often collected online in real-time – and model errors may lead to drastic damages of the system.
Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples.
As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed.
DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility.
The resulting DeLaN network performs very well at robot tracking control.
The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time.

assistant: text: This paper introduces a physics prior for Deep Learning and applies the resulting network topology for model-based control.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Chart Auto-Encoders for Manifold Structured  Data

Paper content:
Auto-encoding and generative models have made tremendous successes in image and signal representation learning and generation.
These models, however, generally employ the full Euclidean space or a bounded subset (such as $[0,1]^l$) as the latent space, whose trivial geometry is often too simplistic to meaningfully reflect the structure of the data.
This paper aims at exploring a nontrivial geometric structure of the latent space for better data representation.
Inspired by differential geometry, we propose \textbf{Chart Auto-Encoder (CAE)}, which captures the manifold structure of the data with multiple charts and transition functions among them.
CAE translates the mathematical definition of manifold through parameterizing the entire data set as a collection of overlapping charts, creating local latent representations.
These representations are an enhancement of the single-charted latent space commonly employed in auto-encoding models, as they reflect the intrinsic structure of the manifold.
Therefore, CAE achieves a more accurate approximation of data and generates realistic new ones.
We conduct experiments with synthetic and real-life data to demonstrate the effectiveness of the proposed CAE.

assistant: text: Manifold-structured latent space for generative models
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Inpainting Network Applications on Seismic Image Compression

Paper content:
The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.
An appropriately designed Wasserstein generative adversarial network is designed based on a generative adversarial network architecture trained on several historical surveys, capable of learning the statistical properties of the seismic wavelets.
The usage of validating and performance testing of compressive sensing are three steps.
First, the existence of a sparse representation with different compression rates for seismic surveys is studied.
Then, non-uniform samplings are studied, using the proposed methodology.
Finally, recommendations for non-uniform seismic survey grid, based on the evaluation of reconstructed seismic images and metrics, is proposed.
The primary goal of the proposed deep learning model is to provide the foundations of an optimal design for seismic acquisition, with less loss in imaging quality.
Along these lines, a compressive sensing design of a non-uniform grid over an asset in Gulf of Mexico, versus a traditional seismic survey grid which collects data uniformly at every few feet, is suggested, leveraging the proposed method.

assistant: text: Improved a GAN based pixel inpainting network for compressed seismic image recovery andproposed a non-uniform sampling survey recommendatio, which can be easily applied to medical and other domains for compressive sensing technique.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Paper content:
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters.
We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases.
We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer.
We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism.
We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function.
Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities.
Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values.
Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.
We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).

assistant: text: We use a hypernetwork to predict optimal weights given hyperparameters, and jointly train everything together.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Collaborative Generated Hashing for Market Analysis and Fast Cold-start Recommendation

Paper content:
Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems.
Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space.
This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones.
Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings.
In addition, CGH initiates a new marketing strategy through mining potential users by a generative step.
To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data.
Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.

assistant: text: It can generate effective hash codes for efficient cold-start recommendation and meanwhile provide a feasible marketing strategy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analysis of Video Feature Learning in Two-Stream CNNs on the Example of Zebrafish Swim Bout Classification

Paper content:
Semmelhack et al. (2014) have achieved high classification accuracy in distinguishing swim bouts of zebrafish using a Support Vector Machine (SVM).
Convolutional Neural Networks (CNNs) have reached superior performance in various image recognition tasks over SVMs, but these powerful networks remain a black box.
Reaching better transparency helps to build trust in their classifications and makes learned features interpretable to experts.
Using a recently developed technique called Deep Taylor Decomposition, we generated heatmaps to highlight input regions of high relevance for predictions.
We find that our CNN makes predictions by analyzing the steadiness of the tail's trunk, which markedly differs from the manually extracted features used by Semmelhack et al. (2014).
We further uncovered that the network paid attention to experimental artifacts.
Removing these artifacts ensured the validity of predictions.
After correction, our best CNN beats the SVM by 6.12%, achieving a classification accuracy of 96.32%.
Our work thus demonstrates the utility of AI explainability for CNNs.

assistant: text: We demonstrate the utility of a recent AI explainability technique by visualizing the learned features of a CNN trained on binary classification of zebrafish movements.
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.
We approached the challenging problem of modeling natural video by introducing a GAN capable of capturing the complexity of a large video dataset.
We showed that on UCF-101 and frame-conditional Kinetics-600 it quantitatively achieves the new state of the art, alongside qualitatively producing video synthesis samples with high complexity and diversity.
We further wish to emphasize the benefit of training generative models on large and complex video datasets, such as Kinetics-600, and envisage the strong baselines we established on this dataset with DVD-GAN will be used as a reference point by the generative modeling community moving forward.
While much remains to be done before realistic videos can be consistently generated in an unconstrained setting, we believe DVD-GAN is a step in that direction.
A EXPERIMENT METHODOLOGY

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: Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

Paper content:
The ability to generalize quickly from few observations is crucial for intelligent systems.
In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered.
These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall.
We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint.
In addition, its memory compression allows it to scale to thousands of unknown labels.
Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification.
In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.

assistant: text: We introduce a model which generalizes quickly from few observations by storing surprising information and attending over the most relevant data at each time point.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Represent Programs with Graphs

Paper content:
Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax.
For example, long-range dependencies induced by using the same variable or function in distant locations are often not considered.
We propose to use graphs to represent both the syntactic and semantic structure of code and use graph-based deep learning methods to learn to reason over program structures.
In this work, we present how to construct graphs from source code and how to scale Gated Graph Neural Networks training to such large graphs.
We evaluate our method on two tasks: VarNaming, in which a network attempts to predict the name of a variable given its usage, and VarMisuse, in which the network learns to reason about selecting the correct variable that should be used at a given program location.
Our comparison to methods that use less structured program representations shows the advantages of modeling known structure, and suggests that our models learn to infer meaningful names and to solve the VarMisuse task in many cases.
Additionally, our testing showed that VarMisuse identifies a number of bugs in mature open-source projects.

assistant: text: Programs have structure that can be represented as graphs, and graph neural networks can learn to find bugs on such graphs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

Paper content:
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems,
including agents that can move with skill and agility through their environment.
An open problem in this setting is that of developing good strategies for integrating or merging policies
for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution.
We extend policy distillation methods to the continuous action setting and leverage this technique to combine \expert policies,
as evaluated in the domain of simulated bipedal locomotion across different classes of terrain.
We also introduce an input injection method for augmenting an existing policy network to exploit new input features.
Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills.
The combination of these methods allows a policy to be incrementally augmented with new skills.
We compare our progressive learning and integration via distillation (PLAID) method
against three alternative baselines.

assistant: text: A continual learning method that uses distillation to combine expert policies and transfer learning to accelerate learning new skills.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Bayesian Perspective on Generalization and Stochastic Gradient Descent

Paper content:
We consider two questions at the heart of machine learning; how can we predict if a minimum will generalize to the test set, and why does stochastic gradient descent find minima that generalize well?
Our work responds to \citet{zhang2016understanding}, who showed deep neural networks can easily memorize randomly labeled training data, despite generalizing well on real labels of the same inputs.
We show that the same phenomenon occurs in small linear models.
These observations are explained by the Bayesian evidence, which penalizes sharp minima but is invariant to model parameterization.
We also demonstrate that, when one holds the learning rate fixed, there is an optimum batch size which maximizes the test set accuracy.
We propose that the noise introduced by small mini-batches drives the parameters towards minima whose evidence is large.
Interpreting stochastic gradient descent as a stochastic differential equation, we identify the ``noise scale" $g = \epsilon (\frac{N}{B} - 1) \approx \epsilon N/B$, where $\epsilon$ is the learning rate, $N$ the training set size and $B$ the batch size.
Consequently the optimum batch size is proportional to both the learning rate and the size of the training set, $B_{opt} \propto \epsilon N$.
We verify these predictions empirically.

assistant: text: Generalization is strongly correlated with the Bayesian evidence, and gradient noise drives SGD towards minima whose evidence is large.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Wasserstein Barycenter Model Ensembling

Paper content:
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
Optimal transport metrics, such as the Wasserstein distance, allow incorporating semantic side information such as word embeddings.
Using W. barycenters to find the consensus between models allows us to balance confidence and semantics in finding the agreement between the models.
We show applications of Wasserstein ensembling in attribute-based classification, multilabel learning and image captioning generation.
These results show that the W. ensembling is a viable alternative to the basic geometric or arithmetic mean ensembling.

assistant: text: we propose to use Wasserstein barycenters for semantic model ensembling
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fast Sparse ConvNets

Paper content:
Historically, the pursuit of efficient inference has been one of the driving forces be-hind the research into new deep learning architectures and building blocks.
Some of the recent examples include:  the squeeze-and-excitation module of (Hu et al.,2018), depthwise separable convolutions in Xception (Chollet, 2017), and the inverted bottleneck in MobileNet v2 (Sandler et al., 2018).
Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field.
In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts.
While the idea of using sparsity to decrease the parameter count is not new (Mozer & Smolensky, 1989), the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains.
We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open-source for the benefit of the community.
Equipped with our efficient implementation of sparse primitives,  we show that sparse versions of MobileNet  v1 and MobileNet  v2  architectures  substantially outperform strong dense baselines on the efficiency-accuracy curve.
On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.1−2.2x – equivalent to approximately one entire generation of improvement.
We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures.

assistant: text: Sparse MobileNets are faster than Dense ones with the appropriate kernels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-View Summarization and Activity Recognition Meet Edge Computing in IoT Environments

Paper content:
Multi-view video summarization (MVS) lacks researchers’ attention due to their major challenges of inter-view correlations and overlapping of cameras.
Most of the prior MVS works are offline, relying on only summary, needing extra communication bandwidth and transmission time with no focus on uncertain environments.
Different from the existing methods, we propose edge intelligence based MVS and spatio-temporal features based activity recognition for IoT environments.
We segment the multi-view videos on each slave device over edge into shots using light-weight CNN object detection model and compute mutual information among them to generate summary.
Our system does not rely on summary only but encode and transmit it to a master device with neural computing stick (NCS) for intelligently computing inter-view correlations and efficiently recognizing activities, thereby saving computation resources, communication bandwidth, and transmission time.
Experiments report an increase of 0.4 in F-measure score on MVS Office dataset as well as 0.2% and 2% increase in activity recognition accuracy over UCF-50 and YouTube 11 datasets, respectively, with lower storage and transmission time compared to state-of-the-art.
The time complexity is decreased from 1.23 to 0.45 secs for a single frame processing, thereby generating 0.75 secs faster MVS.
Furthermore, we made a new dataset by synthetically adding fog to an MVS dataset to show the adaptability of our system for both certain and uncertain surveillance environments.

assistant: text: An efficient multi-view video summarization scheme advanced to activity recognition in IoT environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DeepOBS: A Deep Learning Optimizer Benchmark Suite

Paper content:
Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area.
Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies for deep learning.
We suggest routines and benchmarks for stochastic optimization, with special focus on the unique aspects of deep learning, such as stochasticity, tunability and generalization.
As the primary contribution, we present DeepOBS, a Python package of deep learning optimization benchmarks.
The package addresses key challenges in the quantitative assessment of stochastic optimizers, and automates most steps of benchmarking.
The library includes a wide and extensible set of ready-to-use realistic optimization problems, such as training Residual Networks for image classification on ImageNet or character-level language prediction models, as well as popular classics like MNIST and CIFAR-10.
The package also provides realistic baseline results for the most popular optimizers on these test problems, ensuring a fair comparison to the competition when benchmarking new optimizers, and without having to run costly experiments.
It comes with output back-ends that directly produce LaTeX code for inclusion in academic publications.
It supports TensorFlow and is available open source.

assistant: text: We provide a software package that drastically simplifies, automates, and improves the evaluation of deep learning optimizers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robustness and/or Redundancy Emerge in Overparametrized Deep Neural Networks

Paper content:
Deep neural networks (DNNs) perform well on a variety of tasks despite the fact that most used in practice are vastly overparametrized and even capable of perfectly fitting randomly labeled data.
Recent evidence suggests that developing "compressible" representations is key for adjusting the complexity of overparametrized networks to the task at hand and avoiding overfitting (Arora et al., 2018; Zhou et al., 2018).
In this paper, we provide new empirical evidence that supports this hypothesis, identifying two independent mechanisms that emerge when the network’s width is increased: robustness (having units that can be removed without affecting accuracy) and redundancy (having units with similar activity).
In a series of experiments with AlexNet, ResNet and Inception networks in the CIFAR-10 and ImageNet datasets, and also using shallow networks with synthetic data, we show that DNNs consistently increase either their robustness, their redundancy, or both at greater widths for a comprehensive set of hyperparameters.
These results suggest that networks in the deep learning regime adjust their effective capacity by developing either robustness or redundancy.

assistant: text: Probing robustness and redundancy in deep neural networks reveals capacity-constraining features which help to explain non-overfitting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FVD: A new Metric for Video Generation

Paper content:
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images.
Following their successful application in image processing and representation learning, an important next step is to consider videos.
Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects.
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
To this extent we propose Fréchet Video Distance (FVD), a new metric for generative models of video based on FID.
We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos.

assistant: text: We propose FVD: a new metric for generative models of video based on FID. A large-scale human study confirms that FVD correlates well with qualitative human judgment of generated videos.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Capsules with Inverted Dot-Product Attention Routing

Paper content:
We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote.
Unlike previously proposed routing algorithms, the parent's ability to reconstruct the child is not explicitly taken into account to update the routing probabilities.
This simplifies the routing procedure and improves performance on benchmark datasets such as CIFAR-10 and CIFAR-100.
The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces sequential iterative routing with concurrent iterative routing.
Besides outperforming existing capsule networks, our model performs at-par with a powerful CNN (ResNet-18), using less than 25% of the parameters.
On a different task of recognizing digits from overlayed digit images, the proposed capsule model performs favorably against CNNs given the same number of layers and neurons per layer.
We believe that our work raises the possibility of applying capsule networks to complex real-world tasks.

assistant: text: We present a new routing method for Capsule networks, and it performs at-par with ResNet-18 on CIFAR-10/ CIFAR-100.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A new dog learns old tricks:  RL finds classic optimization algorithms

Paper content:
This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems.
Towards this goal, we introduce a number of key ideas from traditional algorithms and complexity theory.
First, we draw a new connection between primal-dual methods and reinforcement learning.
Next, we introduce the concept of adversarial distributions (universal and high-entropy training sets), which are distributions that encourage the learner to find algorithms that work well in the worst case.
We test our new ideas on a number of optimization problem such as the AdWords problem, the online knapsack problem, and the secretary problem.
Our results indicate that the models have learned behaviours that are consistent with the traditional optimal algorithms for these problems.

assistant: text: By combining ideas from traditional algorithms design and reinforcement learning, we introduce a novel framework for learning algorithms that solve online combinatorial optimization problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discrete Autoencoders for Sequence Models

Paper content:
Recurrent models for sequences have been recently successful at many tasks, especially for language modeling
and machine translation.
Nevertheless, it remains challenging to extract good representations from
these models.
For instance, even though language has a clear hierarchical structure going from characters
through words to sentences, it is not apparent in current language models.
We propose to improve the representation in sequence models by
augmenting current approaches with an autoencoder that is forced to compress
the sequence through an intermediate discrete latent space.
In order to propagate gradients
though this discrete representation we introduce an improved semantic hashing technique.
We show that this technique performs well on a newly proposed quantitative efficiency measure.
We also analyze latent codes produced by the model showing how they correspond to
words and phrases.
Finally, we present an application of the autoencoder-augmented
model to generating diverse translations.

assistant: text: Autoencoders for text with a new method for using discrete latent space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks

Paper content:
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.
This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data.
Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data.
Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications.
However, in the domain of materials science, there is a need to synthesize data with higher order complexity compared to observed samples, and the state-of-the-art cross-domain GANs can not be adapted directly.
In this contribution, we propose a novel GAN called CrystalGAN which generates new chemically stable crystallographic structures with increased domain complexity.
We introduce an original architecture, we provide the corresponding loss functions, and we show that the CrystalGAN generates very reasonable data.
We illustrate the efficiency of the proposed method on a real original problem of novel hydrides discovery that can be further used in development of hydrogen storage materials.

assistant: text: "Generating new chemical materials using novel cross-domain GANs."
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RNNs implicitly implement tensor-product representations

Paper content:
Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies).
Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g.,  sequence positions) and vectors representing fillers (e.g., particular words)
. To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations
. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations
.  By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures
. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated byTPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations

assistant: text: RNNs implicitly implement tensor-product representations, a principled and interpretable method for representing symbolic structures in continuous space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dream to Control: Learning Behaviors by Latent Imagination

Paper content:
To select effective actions in complex environments, intelligent agents need to generalize from past experience.
World models can represent knowledge about the environment to facilitate such generalization.
While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them.
We present Dreamer, a reinforcement learning agent that solves long-horizon tasks purely by latent imagination.
We efficiently learn behaviors by backpropagating analytic gradients of learned state values through trajectories imagined in the compact state space of a learned world model.
On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

assistant: text: We present Dreamer, an agent that learns long-horizon behaviors purely by latent imagination using analytic value gradients.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning

Paper content:
Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training.
However, if our goal is to develop an algorithm that learns as humans do, this setting is far from realistic, and it is essential to develop a methodology that works in a task-free manner.
Meanwhile, among several branches of continual learning, expansion-based methods have the advantage of eliminating catastrophic forgetting by allocating new resources to learn new data.
In this work, we propose an expansion-based approach for task-free continual learning.
Our model, named Continual Neural Dirichlet Process Mixture (CN-DPM), consists of a set of neural network experts that are in charge of a subset of the data.
CN-DPM expands the number of experts in a principled way under the Bayesian nonparametric framework.
With extensive experiments, we show that our model successfully performs task-free continual learning for both discriminative and generative tasks such as image classification and image generation.

assistant: text: We propose an expansion-based approach for task-free continual learning for the first time. Our model consists of a set of neural network experts and expands the number of experts under the Bayesian nonparametric principle.