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

Title: DppNet: Approximating Determinantal Point Processes with Deep Networks

Paper content:
Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.
For this reason, they have gained prominence in many machine learning applications that rely on subset selection.
However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets.
We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors.
We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPP so powerful and unique.
Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.

assistant: text: We approximate Determinantal Point Processes with neural nets; we justify our model theoretically and empirically.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interactive Shape Based Brushing Technique for Trail Sets

Paper content:
Brushing techniques have a long history with the first interactive selection tools appearing in the 1990's.
Since then, many additional techniques have been developed to address selection accuracy, scalability and flexibility issues.
Selection is especially difficult in large datasets where many visual items tangle and create overlapping.
This paper investigates a novel brushing technique which not only relies on the actual brushing location but also on the shape of the brushed area.
Firstly, the user brushes the region where trajectories of interest are visible.
Secondly, the shape of the brushed area is used to select similar items.
Thirdly, the user can adjust the degree of similarity to filter out the requested trajectories.
This technique encompasses two types of comparison metrics, the piece-wise Pearson correlation and the similarity measurement based on information geometry.
We apply it to concrete scenarios with datasets from air traffic control, eye-tracking data and GPS trajectories.

assistant: text: Interactive technique to improve brushing in dense trajectory datasets by taking into account the shape of the brush.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Distilling Neural Networks for Faster and Greener Dependency Parsing

Paper content:
The carbon footprint of natural language processing (NLP) research has been increasing in recent years due to its reliance on large and inefficient neural network implementations.
Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one.
We use teacher-student distillation  to  improve  the  efficiency  of  the  Biaffine  dependency  parser  which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat & Manning, 2016).
When distilling to 20% of the original model’s trainable parameters, we only observe an average decrease of ∼1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.26x (1.21x) faster than the baseline model on CPU (GPU) at inference time.
We also observe a small increase in performance when compressing to 80% for some treebanks.
Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.

assistant: text: We increase the efficiency of neural network dependency parsers with teacher-student distillation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exploring Properties of the Deep Image Prior

Paper content:
The Deep Image Prior (DIP, Ulyanov et al., 2017) is a fascinating recent approach for recovering images which appear natural, yet is not fully understood.
This work aims at shedding some further light on this approach by investigating the properties of the early outputs of the DIP.
First, we show that these early iterations demonstrate invariance to adversarial perturbations by classifying progressive DIP outputs and using a novel saliency map approach.
Next we explore using DIP as a defence against adversaries, showing good potential.
Finally, we examine the adversarial invariancy of the early DIP outputs, and hypothesize that these outputs may remove non-robust image features.
By comparing classification confidence values we show some evidence confirming this hypothesis.

assistant: text: We investigate properties of the recently introduced Deep Image Prior (Ulyanov et al, 2017)
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing

Paper content:
In cooperative multi-agent reinforcement learning (MARL), how to design a suitable reward signal to accelerate learning and stabilize convergence is a critical problem.
The global reward signal assigns the same global reward to all agents without distinguishing their contributions, while the local reward signal provides different local rewards to each agent based solely on individual behavior.
Both of the two reward assignment approaches have some shortcomings: the former might encourage lazy agents, while the latter might produce selfish agents.
In this paper, we study reward design problem in cooperative MARL based on packet routing environments.
Firstly, we show that the above two reward signals are prone to produce suboptimal policies.
Then, inspired by some observations and considerations, we design some mixed reward signals, which are off-the-shelf to learn better policies.
Finally, we turn the mixed reward signals into the adaptive counterparts, which achieve best results in our experiments.
Other reward signals are also discussed in this paper.
As reward design is a very fundamental problem in RL and especially in MARL, we hope that MARL researchers can rethink the rewards used in their systems.

assistant: text: We study reward design problem in cooperative MARL based on packet routing environments. The experimental results remind us to be careful to design the rewards, as they are really important to guide the agent behavior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BERT Goes to Law School: Quantifying the Competitive Advantage of Access to Large Legal Corpora in Contract Understanding

Paper content:
Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text.
In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain.
Demonstrating this outcome is significant for analyzing commercial agreements, because obtaining large legal corpora is challenging due to their confidential nature.
As such, we show that having access to large legal corpora is a competitive advantage for commercial applications, and academic research on analyzing contracts.
Businesses rely on contracts to capture critical obligations with other parties, such as: scope of work, amounts owed, and cancellation policies.
Various efforts have gone into automatically extracting and classifying these terms.
These efforts have usually been modeled as: classification, entity and relation extraction tasks.
In this paper we focus on classification, but in our application we have found that our findings apply equally and sometimes, more profoundly, on other tasks.
Recently, numerous studies have shown the value of fine-tuning language models such as ELMo [3] and BERT [4] to achieve state-of-the-art results [5] on domain specific tasks [6, 7] .
In this paper we investigate and quantify the impact of utilizing a large domain-specific corpus of legal agreements to improve the accuracy of classification models by fine-tuning BERT.
Specifically, we assess:
(i) the performance of a simple model that only uses the pre-trained BERT language model,
(ii) the impact of further fine tuning BERT, and
(iii) how this impact changes as we train on larger corpora.
Ultimately, our investigations show marginal, but valuable, improvements that increase as we grow the size of the legal corpus used to fine-tine BERT -and allow us to confidently claim that not only is this approach valuable for increasing accuracy, but commercial enterprises seeking to create these models will have an edge if they can amass a corpus of legal documents.

assistant: text: Fine-tuning BERT on legal corpora provides marginal, but valuable, improvements on NLP tasks in the legal domain.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scaling shared model governance via model splitting

Paper content:
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation.
Unfortunately, neither of these techniques is applicable to the training of large neural networks due to their large computational and communication overheads.
As a scalable technique for shared model governance, we propose splitting deep learning model between multiple parties.
This paper empirically investigates the security guarantee of this technique, which is introduced as the problem of model completion:  Given the entire training data set or an environment simulator, and a subset of the parameters of a trained deep learning model, how much training is required to recover the model’s original performance?
We define a metric for evaluating the hardness of the model completion problem and study it empirically in both supervised learning on ImageNet and reinforcement learning on Atari and DeepMind Lab.
Our experiments show that (1) the model completion problem is harder in reinforcement learning than in supervised learning because of the unavailability of the trained agent’s trajectories, and (2) its hardness depends not primarily on the number of parameters of the missing part, but more so on their type and location.
Our results suggest that model splitting might be a feasible technique for shared model governance in some settings where training is very expensive.

assistant: text: We study empirically how hard it is to recover missing parts of trained models
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovering the mechanics of hidden neurons

Paper content:
Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding.
This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons.
While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown.
We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing.
During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training.
During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction.
These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.

assistant: text: We report experiments providing strong evidence that a neuron behaves like a binary classifier during training and testing
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hindsight Trust Region Policy Optimization

Paper content:
As reinforcement learning continues to drive machine intelligence beyond its conventional boundary, unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields.
Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment, this paper presents Hindsight Trust Region Policy Optimization (HTRPO), a method that efficiently utilizes interactions in sparse reward conditions to optimize policies within trust region and, in the meantime, maintains learning stability.
Firstly, we theoretically adapt the TRPO objective function, in the form of the expected return of the policy, to the distribution of hindsight data generated from the alternative goals.
Then, we apply Monte Carlo with importance sampling to estimate KL-divergence between two policies, taking the hindsight data as input.
Under the condition that the distributions are sufficiently close, the KL-divergence is approximated by another f-divergence.
Such approximation results in the decrease of variance and alleviates the instability during policy update.
Experimental results on both discrete and continuous benchmark tasks demonstrate that HTRPO converges significantly faster than previous policy gradient methods.
It achieves effective performances and high data-efficiency for training policies in sparse reward environments.

assistant: text: This paper proposes an advanced policy optimization method with hindsight experience for sparse reward reinforcement learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Move with Affordance Maps

Paper content:
The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles.
Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model dynamic objects (such as other agents) or semantic constraints (such as wet floors or doorways).
Learning-based RL agents are an attractive alternative because they can incorporate both semantic and geometric information, but are notoriously sample inefficient, difficult to generalize to novel settings, and are difficult to interpret.
In this paper, we combine the best of both worlds with a modular approach that {\em learns} a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners.
Specifically, we design an agent that learns to predict a spatial affordance map that elucidates what parts of a scene are navigable through active self-supervised experience gathering.
In contrast to most simulation environments that assume a static world, we evaluate our approach in the VizDoom simulator, using large-scale randomly-generated maps containing a variety of dynamic actors and hazards.
We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance.

assistant: text: We address the task of autonomous exploration and navigation using spatial affordance maps that can be learned in a self-supervised manner, these outperform classic geometric baselines while being more sample efficient than contemporary RL algorithms
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Data augmentation techniques, e.g., flipping or cropping, which systematically enlarge the training dataset by explicitly generating more training samples, are effective in improving the generalization performance of deep neural networks.
In the supervised setting, a common practice for data augmentation is to assign the same label to all augmented samples of the same source.
However, if the augmentation results in large distributional discrepancy among them (e.g., rotations), forcing their label invariance may be too difficult to solve and often hurts the performance.
To tackle this challenge, we suggest a simple yet effective idea of learning the joint distribution of the original and self-supervised labels of augmented samples.
The joint learning framework is easier to train, and enables an aggregated inference combining the predictions from different augmented samples for improving the performance.
Further, to speed up the aggregation process, we also propose a knowledge transfer technique, self-distillation, which transfers the knowledge of augmentation into the model itself.
We demonstrate the effectiveness of our data augmentation framework on various fully-supervised settings including the few-shot and imbalanced classification scenarios.

assistant: text: We propose a simple self-supervised data augmentation technique which improves performance of fully-supervised scenarios including few-shot learning and imbalanced classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Board games often rely on visual information such as the location of the game pieces and textual information on cards.
Due to this reliance on visual feedback, blind players are at a disadvantage because they cannot read the cards or see the location of the game pieces and may be unable to play a game without sighted help.
We present Game Changer, an augmented workspace that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.
In this paper, we describe the design of Game Changer and present findings from a user study in which 7 blind participants used Game Changer to play against a sighted partner.
Most players stated the game was more accessible with the additions from Game Changer and felt that Game Changer could be used to augment other games.

assistant: text: Game Changer is a system that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution

Paper content:
Architecture search aims at automatically finding neural architectures that are competitive with architectures designed by human experts.
While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption; (2)most architecture search methods require vast computational resources.
We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method.
We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents.
This is accomplished by using (approximate) network morphism operators for generating children.
The combination of these two contributions allows finding models that are on par or even outperform different-sized NASNets, MobileNets, MobileNets V2 and Wide Residual Networks on CIFAR-10 and ImageNet64x64 within only one week on eight GPUs, which is about 20-40x less compute power than previous architecture search methods that yield state-of-the-art performance.

assistant: text: We propose a method for efficient Multi-Objective Neural Architecture Search based on Lamarckian inheritance and evolutionary algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dependent Bidirectional RNN with Extended-long Short-term Memory

Paper content:
In this work, we first conduct mathematical analysis on the memory, which is
defined as a function that maps an element in a sequence to the current output,
of three RNN cells; namely, the simple recurrent neural network (SRN), the long
short-term memory (LSTM) and the gated recurrent unit (GRU).
Based on the
analysis, we propose a new design, called the extended-long short-term memory
(ELSTM), to extend the memory length of a cell.
Next, we present a multi-task
RNN model that is robust to previous erroneous predictions, called the dependent
bidirectional recurrent neural network (DBRNN), for the sequence-in-sequenceout
(SISO) problem.
Finally, the performance of the DBRNN model with the
ELSTM cell is demonstrated by experimental results.

assistant: text: A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TrojanNet: Exposing the Danger of Trojan Horse Attack on Neural Networks

Paper content:
The complexity of large-scale neural networks can lead to poor understanding of their internal  details.
We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks.
Our novel framework hides the existence of a malicious network within a benign transport network.
Our attack is flexible, easy to execute, and difficult to detect.
We prove theoretically that the malicious network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise.
Our attack exposes an important, previously unknown loophole that unveils a new direction in machine learning security.

assistant: text: Parameters of a trained neural network can be permuted to produce a completely separate model for a different task, enabling the embedding of Trojan horse networks inside another network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Global reasoning network for image super-resolution

Paper content:
Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods.
However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information.
This causes problems for further performance improvement.
In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning.
Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB).
They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details.
Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation.
Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods.
Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.

assistant: text: A state-of-the-art model based on global reasoning for image super-resolution
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Diverse High-Resolution Images with VQ-VAE

Paper content:
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.
To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before.
We use simple feed-forward encoder and decoder networks, thus our model is an attractive candidate for applications where the encoding and decoding speed is critical.
Additionally, this  allows us to only sample autoregressively in the compressed latent space, which is an order of magnitude faster than sampling in the pixel space, especially for large images.
We demonstrate that a multi-scale hierarchical organization of  VQ-VAE, augmented with powerful priors over the latent codes, is able to generate samples with quality that rivals that of state of the art Generative Adversarial Networks on multifaceted datasets such as ImageNet, while not suffering from GAN's known shortcomings such as mode collapse and lack of diversity.

assistant: text: scale and enhance VQ-VAE with powerful priors to generate near realistic images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits.
Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 do not tell us \emph{why} or \emph{how} a particular method is better and how dataset biases influence the choices of model design.
In this paper, we present a general methodology for {\emph{interpretable}} evaluation of NLP systems and choose the task of named entity recognition (NER) as a case study, which is a core task of identifying people, places, or organizations in text.
The proposed evaluation method enables us to interpret the \textit{model biases}, \textit{dataset biases}, and how the \emph{differences in the datasets} affect the design of the models, identifying the strengths and weaknesses of current approaches.
By making our {analysis} tool available, we make it easy for future researchers to run similar analyses and drive the progress in this area.

assistant: text: We propose a generalized evaluation methodology to interpret model biases, dataset biases, and their correlation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantum Expectation-Maximization for Gaussian Mixture Models

Paper content:
The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning.
It is often used as an efficient way to solve Maximum Likelihood (ML) and Maximum A Posteriori estimation problems, especially for models with latent variables.
It is also the algorithm of choice to fit mixture models: generative models that represent unlabelled points originating from $k$ different processes, as samples from $k$ multivariate distributions.
In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model.
Given quantum access to a dataset of $n$ vectors of dimension $d$, our algorithm has convergence and precision guarantees similar to the classical algorithm, but the runtime is only polylogarithmic in the number of elements in the training set, and is polynomial in other parameters - as the dimension of the feature space, and the number of components in the mixture.
We generalize further the algorithm by fitting any mixture model of base distributions in the exponential family.
We discuss the performance of the algorithm on datasets that are expected to be classified successfully by those algorithms, arguing that on those cases we can give strong guarantees on the runtime.

assistant: text: It's the quantum algorithm for Expectation Maximization. It's fast: the runtime depends only polylogarithmically on the number of elements in the dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Outlier Detection from Image Data

Paper content:
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.
In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier.
We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well.
This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification.
To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images.
Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem.

assistant: text: A novel approach that detects outliers from image data,  while preserving the classification accuracy of image classification
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evaluating Lossy Compression Rates of Deep Generative Models

Paper content:
Deep generative models have achieved remarkable progress in recent years.
Despite this progress, quantitative evaluation and comparison of generative models remains as one of the important challenges.
One of the most popular metrics for evaluating generative models is the log-likelihood.
While the direct computation of log-likelihood can be intractable, it has been recently shown that the log-likelihood of some of the most interesting generative models such as variational autoencoders (VAE) or generative adversarial networks (GAN) can be efficiently estimated using annealed importance sampling (AIS).
In this work, we argue that the log-likelihood metric by itself cannot represent all the different performance characteristics of generative models, and propose to use rate distortion curves to evaluate and compare deep generative models.
We show that we can approximate the entire rate distortion curve using one single run of AIS for roughly the same computational cost as a single log-likelihood estimate.
We evaluate lossy compression rates of different deep generative models such as VAEs, GANs (and its variants) and adversarial autoencoders (AAE) on MNIST and CIFAR10, and arrive at a number of insights not obtainable from log-likelihoods alone.

assistant: text: We study rate distortion approximations for evaluating deep generative models, and show that rate distortion curves provide more insights about the model than the log-likelihood alone while requiring roughly the same computational cost.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Autostacker: an Automatic Evolutionary Hierarchical  Machine Learning System

Paper content:
This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker.
Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm.
Neither prior domain knowledge about the data nor feature preprocessing is needed.
We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC).
By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time.
These pipelines can be used as is or as a starting point for human experts to build on.
By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures.
As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.

assistant: text: Automate machine learning system with efficient search algorithm and innovative structure to provide better model baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Strategy Synthesis in POMDPs via Game-Based Abstractions

Paper content:
Partially observable Markov decision processes (POMDPs) are a natural model for scenarios where one has to deal with incomplete knowledge and random events.
Applications include, but are not limited to, robotics and motion planning.
However, many relevant properties of POMDPs are either undecidable or very expensive to compute in terms of both runtime and memory consumption.
In our work, we develop a game-based abstraction method that is able to deliver safe bounds and tight
  approximations for important sub-classes of such properties.
We discuss the theoretical implications and showcase the applicability of our results on a broad spectrum of benchmarks.

assistant: text: This paper provides a game-based abstraction scheme to compute provably sound policies for POMDPs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Deep Learning via Stochastic Gradient MCMC with a Stochastic Approximation Adaptation

Paper content:
We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables.
Inspired by dropout, a popular tool for regularization and model ensemble, we assign sparse priors to the weights in deep neural networks (DNN) in order to achieve automatic “dropout” and avoid over-fitting.
By alternatively sampling from posterior distribution through stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and optimizing latent variables via stochastic approximation (SA), the trajectory of the target weights is proved to converge to the true posterior distribution conditioned on optimal latent variables.
This ensures a stronger regularization on the over-fitted parameter space and more accurate uncertainty quantification on the decisive variables.
Simulations from large-p-small-n regressions showcase the robustness of this method when applied to models with latent variables.
Additionally, its application on the convolutional neural networks (CNN) leads to state-of-the-art performance on MNIST and Fashion MNIST datasets and improved resistance to adversarial attacks.

assistant: text: a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Riemannian Adaptive Optimization Methods

Paper content:
Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.
However, some of the most popular of these optimization tools - namely Adam, Adagrad and the more recent Amsgrad - remain to be generalized to Riemannian manifolds.
We discuss the difficulty of generalizing such adaptive schemes to the most agnostic Riemannian setting, and then provide algorithms and convergence proofs for geodesically convex objectives in the particular case of a product of Riemannian manifolds, in which adaptivity is implemented across manifolds in the cartesian product.
Our generalization is tight in the sense that choosing the Euclidean space as Riemannian manifold yields the same algorithms and regret bounds as those that were already known for the standard algorithms.
Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the WordNet taxonomy in the Poincare ball.

assistant: text: Adapting Adam, Amsgrad, Adagrad to Riemannian manifolds.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Graph Infomax

Paper content:
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.
The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks.
In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups.
We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.
We have presented Deep Graph Infomax (DGI), a new approach for learning unsupervised representations on graph-structured data.
By leveraging local mutual information maximization across the graph's patch representations, obtained by powerful graph convolutional architectures, we are able to obtain node embeddings that are mindful of the global structural properties of the graph.
This enables competitive performance across a variety of both transductive and inductive classification tasks, at times even outperforming relevant supervised architectures.

assistant: text: A new method for unsupervised representation learning on graphs, relying on maximizing mutual information between local and global representations in a graph. State-of-the-art results, competitive with supervised learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unlabeled Disentangling of GANs with Guided Siamese Networks

Paper content:
Disentangling underlying generative factors of a data distribution is important for interpretability and generalizable representations.
In this paper,  we introduce two novel disentangling methods.
Our first method, Unlabeled Disentangling GAN (UD-GAN, unsupervised), decomposes the latent noise by generating similar/dissimilar image pairs and it learns a distance metric on these pairs with siamese networks and a contrastive loss.
This pairwise approach provides consistent representations for similar data points.
Our second method (UD-GAN-G, weakly supervised) modifies the UD-GAN with user-defined guidance functions, which restrict the information that goes into the siamese networks.
This constraint helps UD-GAN-G to focus on the desired semantic variations in the data.
We  show  that  both  our  methods  outperform  existing  unsupervised approaches in quantitative metrics that measure semantic accuracy of the learned representations.
In addition, we illustrate that simple guidance functions we use in UD-GAN-G allow us to directly capture the desired variations in the data.

assistant: text: We use Siamese Networks to guide and disentangle the generation process in GANs without labeled data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Meta-Learning for Reinforcement Learning

Paper content:
Meta-learning algorithms learn to acquire new tasks more quickly from past experience.
In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks.
The performance of meta-learning algorithms depends on the tasks available for meta-training: in the same way that supervised learning generalizes best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks.
In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design.
If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated.
In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning.
We motivate and describe a general recipe for unsupervised meta-reinforcement learning, and present an instantiation of this approach.
Our conceptual and theoretical contributions consist of formulating the unsupervised meta-reinforcement learning problem and describing how task proposals based on mutual information can in principle be used to train optimal meta-learners.
Our experimental results indicate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design and significantly exceeds the performance of learning from scratch.

assistant: text: Meta-learning on self-proposed task distributions to speed up reinforcement learning without human specified task distributions
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph Classification with 2D Convolutional Neural Networks

Paper content:
Graph classification is currently dominated by graph kernels, which, while powerful, suffer some significant limitations.
Convolutional Neural Networks (CNNs) offer a very appealing alternative.
However, processing graphs with CNNs is not trivial.
To address this challenge, many sophisticated extensions of CNNs have recently been proposed.
In this paper, we reverse the problem: rather than proposing yet another graph CNN model, we introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs.
Despite its simplicity, our method proves very competitive to state-of-the-art graph kernels and graph CNNs, and outperforms them by a wide margin on some datasets.
It is also preferable to graph kernels in terms of time complexity.
Code and data are publicly available.

assistant: text: We introduce a novel way to represent graphs as multi-channel image-like structures that allows them to be handled by vanilla 2D CNNs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Contextual and neural representations of sequentially complex animal vocalizations

Paper content:
Holistically exploring the perceptual and neural representations underlying animal communication has traditionally been very difficult because of the complexity of the underlying signal.
We present here a novel set of techniques to project entire communicative repertoires into low dimensional spaces that can be systematically sampled from, exploring the relationship between perceptual representations, neural representations, and the latent representational spaces learned by machine learning algorithms.
We showcase this method in one ongoing experiment studying sequential and temporal maintenance of context in songbird neural and perceptual representations of syllables.
We further discuss how studying the neural mechanisms underlying the maintenance of the long-range information content present in birdsong can inform and be informed by machine sequence modeling.

assistant: text: We  compare perceptual, neural, and modeled representations of animal communication using machine learning, behavior, and physiology.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

Paper content:
Autoencoders provide a powerful framework for learning compressed representations by encoding all of the information needed to reconstruct a data point in a latent code.
In some cases, autoencoders can "interpolate": By decoding the convex combination of the latent codes for two datapoints, the autoencoder can produce an output which semantically mixes characteristics from the datapoints.
In this paper, we propose a regularization procedure which encourages interpolated outputs to appear more realistic by fooling a critic network which has been trained to recover the mixing coefficient from interpolated data.
We then develop a simple benchmark task where we can quantitatively measure the extent to which various autoencoders can interpolate and show that our regularizer dramatically improves interpolation in this setting.
We also demonstrate empirically that our regularizer produces latent codes which are more effective on downstream tasks, suggesting a possible link between interpolation abilities and learning useful representations.

assistant: text: We propose a regularizer that improves interpolation and autoencoders and show that it also improves the learned representation for downstream tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural MMO: A massively multiplayer game environment for intelligent agents

Paper content:
We present an artificial intelligence research platform inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs).
We demonstrate how this platform can be used to study behavior and learning in large populations of neural agents.
Unlike currently popular game environments, our platform supports persistent environments, with variable number of agents, and open-ended task descriptions.
The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources.
Our platform aims to simulate this setting in microcosm: we conduct a series of experiments to test how large-scale multiagent competition can incentivize the development of skillful behavior.
We find that population size magnifies the complexity of the behaviors that emerge and results in agents that out-compete agents trained in smaller populations.

assistant: text: An MMO-inspired research game platform for studying emergent behaviors of large populations in a complex environment