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

Title: Learning  agents with prioritization and parameter noise in continuous state and action space

Paper content:
Reinforcement  Learning  (RL) problem can be solved in two different ways - the Value function-based approach and the policy optimization-based approach - to eventually arrive at an optimal policy for the given environment.
One of the recent breakthroughs in reinforcement learning is the use of deep neural networks as function approximators to approximate the value function or q-function in a reinforcement learning scheme.
This has led to results with agents automatically learning how to play games like alpha-go showing better-than-human performance.
Deep Q-learning networks (DQN) and  Deep Deterministic Policy Gradient (DDPG) are two such methods that have shown state-of-the-art results in recent times.
Among the many variants of RL, an important class of problems is where the state and action spaces are continuous --- autonomous robots, autonomous vehicles, optimal control are all examples of such problems that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces.
In this paper, we adapt and combine approaches such as DQN and DDPG in novel ways to outperform the earlier results for continuous state and action space problems.
We believe these results are a valuable addition to the fast-growing body of results on Reinforcement Learning, more so for continuous state and action space problems.

assistant: text: Improving the performance of an RL agent in the continuous action and state space domain by using prioritised experience replay and parameter noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Paper content:
Understanding the flow of information in Deep Neural Networks (DNNs) is a challenging problem that has gain increasing attention over the last few years.
While several methods have been proposed to explain network predictions, there have been only a few attempts to compare them from a theoretical perspective.
What is more, no exhaustive empirical comparison has been performed in the past.
In this work we analyze four gradient-based attribution methods and formally prove conditions of equivalence and approximation between them.
By reformulating two of these methods, we construct a unified framework which enables a direct comparison, as well as an easier implementation.
Finally, we propose a novel evaluation metric, called Sensitivity-n and test the gradient-based attribution methods alongside with a simple perturbation-based attribution method on several datasets in the domains of image and text classification, using various network architectures.

assistant: text: Four existing backpropagation-based attribution methods are fundamentally similar. How to assess it?
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
In this paper, we explore meta-learning for few-shot text classification.
Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable across learning tasks.
However, directly applying this approach to text is challenging–lexical features highly informative for one task maybe insignificant for another.
Thus, rather than learning solely from words, our model also leverages their distributional signatures, which encode pertinent word occurrence patterns.
Our model is trained within a meta-learning framework to map these signatures into attention scores, which are then used to weight the lexical representations of words.
We demonstrate that our model consistently outperforms prototypical networks learned on lexical knowledge (Snell et al., 2017) in both few-shot text classification and relation classification by a significant margin across six benchmark datasets (19.96% on average in 1-shot classification).

assistant: text: Meta-learning methods used for vision, directly applied to NLP, perform worse than nearest neighbors on new classes; we can do better with distributional signatures.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: HexaConv

Paper content:
The effectiveness of Convolutional Neural Networks stems in large part from their ability to exploit the translation invariance that is inherent in many learning problems.
Recently, it was shown that CNNs can exploit other invariances, such as rotation invariance, by using group convolutions instead of planar convolutions.
However, for reasons of performance and ease of implementation, it has been necessary to limit the group convolution to transformations that can be applied to the filters without interpolation.
Thus, for images with square pixels, only integer translations, rotations by multiples of 90 degrees, and reflections are admissible.
Whereas the square tiling provides a 4-fold rotational symmetry, a hexagonal tiling of the plane has a 6-fold rotational symmetry.
In this paper we show how one can efficiently implement planar convolution and group convolution over hexagonal lattices, by re-using existing highly optimized convolution routines.
We find that, due to the reduced anisotropy of hexagonal filters, planar HexaConv provides better accuracy than planar convolution with square filters, given a fixed parameter budget.
Furthermore, we find that the increased degree of symmetry of the hexagonal grid increases the effectiveness of group convolutions, by allowing for more parameter sharing.
We show that our method significantly outperforms conventional CNNs on the AID aerial scene classification dataset, even outperforming ImageNet pre-trained models.

assistant: text: We introduce G-HexaConv, a group equivariant convolutional neural network on hexagonal lattices.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Minigo: A Case Study in Reproducing Reinforcement Learning Research

Paper content:
The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field.
In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game.
We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs.
The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure.
With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs.
We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.

assistant: text: We reproduced AlphaZero on Google Cloud Platform
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Compositional Koopman Operators for Model-Based Control

Paper content:
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.
The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods.
Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators.
These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects.
In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects.
The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal.
Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines.

assistant: text: Learning compositional Koopman operators for efficient system identification and model-based control.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph Wavelet Neural Network

Paper content:
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.
Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost.
Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution.
The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed.

assistant: text: We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcoming of previous spectral graph CNN methods that depend on graph Fourier transform.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Revisiting The Master-Slave Architecture In Multi-Agent Deep Reinforcement Learning

Paper content:
Many tasks in artificial intelligence require the collaboration of multiple agents.
We exam deep reinforcement learning for multi-agent domains.
Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; and the centralized perspective, where one assumes there is a larger model controlling all agents.
In this regard, we revisit the idea of the master-slave architecture by incorporating both perspectives within one framework.
Such a hierarchical structure naturally leverages advantages from one another.
The idea of combining both perspective is intuitive and can be well motivated from many real world systems, however, out of a variety of possible realizations, we highlights three key ingredients, i.e. composed action representation, learnable communication and independent reasoning.
With network designs to facilitate these explicitly, our proposal consistently outperforms latest competing methods both in synthetics experiments and when applied to challenging StarCraft  micromanagement tasks.

assistant: text: We revisit the idea of the master-slave architecture in multi-agent deep reinforcement learning and outperforms state-of-the-arts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Hierarchical Discrete Variational Autoencoders

Paper content:
Variational Autoencoders (VAEs) have proven to be powerful latent variable models.
How- ever, the form of the approximate posterior can limit the expressiveness of the model.
Categorical distributions are flexible and useful building blocks for example in neural memory layers.
We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers.
The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent.
We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets.
Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective.
We introduce a formal definition and conduct a preliminary theoretical and empirical study of the bias.

assistant: text: In this paper, we introduce a discrete hierarchy of categorical latent variables that we train using the Concrete/Gumbel-Softmax relaxation and we derive an upper bound for the absolute difference between the unbiased and the biased objective.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on Smoothly Varying Weight Hypothesis

Paper content:
Due to a resource-constrained environment, network compression has become an important part of deep neural networks research.
In this paper, we propose a new compression method, Inter-Layer Weight Prediction (ILWP) and quantization method which quantize the predicted residuals between the weights in all convolution layers based on an inter-frame prediction method in conventional video coding schemes.
Furthermore, we found a phenomenon Smoothly Varying Weight Hypothesis (SVWH) which is that the weights in adjacent convolution layers share strong similarity in shapes and values, i.e., the weights tend to vary smoothly along with the layers.
Based on SVWH, we propose a second ILWP and quantization method which quantize the predicted residuals between the weights in adjacent convolution layers.
Since the predicted weight residuals tend to follow Laplace distributions with very low variance, the weight quantization can more effectively be applied, thus producing more zero weights and enhancing the weight compression ratio.
In addition, we propose a new inter-layer loss for eliminating non-texture bits, which enabled us to more effectively store only texture bits.
That is, the proposed loss regularizes the weights such that the collocated weights between the adjacent two layers have the same values.
Finally, we propose an ILWP with an inter-layer loss and quantization method.
Our comprehensive experiments show that the proposed method achieves a much higher weight compression rate at the same accuracy level compared with the previous quantization-based compression methods in deep neural networks.

assistant: text: We propose a new compression method, Inter-Layer Weight Prediction (ILWP) and quantization method which quantize the predicted residuals between the weights in convolution layers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Learning with Disentanglement-PyTorch

Paper content:
Unsupervised learning of disentangled representations is an open problem in machine learning.
The Disentanglement-PyTorch library is developed to facilitate research, implementation, and testing of new variational algorithms.
In this modular library, neural architectures, dimensionality of the latent space, and the training algorithms are fully decoupled, allowing for independent and consistent experiments across variational methods.
The library handles the training scheduling, logging, and visualizations of reconstructions and latent space traversals.
It also evaluates the encodings based on various disentanglement metrics.
The library, so far, includes implementations of the following unsupervised algorithms VAE, Beta-VAE, Factor-VAE, DIP-I-VAE, DIP-II-VAE, Info-VAE, and Beta-TCVAE, as well as conditional approaches such as CVAE and IFCVAE.
The library is compatible with the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd and was used to compete in the first and second stages of the challenge, where it was ranked among the best few participants.
There are two overlapping avenues in representation learning.
One focuses on learning task-specific transformations often optimized towards specific domains and applications.
The other approach learns the intrinsic factors of variation, in a disentangled and taskinvariant fashion.
The unsupervised disentanglement of latent factors, where changes in a single factor of variation shifts the latent encoding in a single direction, is an open problem of representation learning (Bengio et al., 2013; Lake et al., 2016) .
Disentangled representations are valuable in few-shot learning, reinforcement learning, transfer learning, as well as semisupervised learning (Bengio et al., 2013; Peters et al., 2017; .
In this work, we developed a library based on the functionalities of the PyTorch framework, which facilitates research, implementation, and testing of new variational algorithms focusing on representation learning and disentanglement.
The library branches from the Disentanglement Challenge of NeurIPS 2019, hosted on AICrowd (aicrowd.com), and was used to compete in the first and second stages of the challenge where it was highly ranked.
The Disentanglement-PyTorch library is released under the GNU General Public License at https://github.com/amir-abdi/disentanglement-pytorch.

assistant: text: Disentanglement-PyTorch is a library for variational representation learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Multi-Level Hierarchies with Hindsight

Paper content:
Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions.
In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously.
Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies.
In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies.
The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal.
We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods.
Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.

assistant: text: We introduce the first Hierarchical RL approach to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EfferenceNets for latent space planning

Paper content:
Planning in high-dimensional space remains a challenging problem, even with recent advances in algorithms and computational power.
We are inspired by efference copy and sensory reafference theory from neuroscience.
Our aim is to allow agents to form mental models of their environments for planning.
The cerebellum is emulated with a two-stream, fully connected, predictor network.
The network receives as inputs the efference as well as the features of the current state.
Building on insights gained from knowledge distillation methods, we choose as our features the outputs of a pre-trained network,  yielding a compressed representation of the current state.
The representation is chosen such that it allows for fast search using classical graph search algorithms.
We display the effectiveness of our approach on a viewpoint-matching task using a modified best-first search algorithm.

assistant: text: We present a neuroscience-inspired method based on neural networks for latent space search
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Paper content:
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks.
However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning.
Both of these challenges severely limit the applicability of such methods to complex, real-world domains.
In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework.
In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible.
Prior deep RL methods based on this framework have been formulated as either off-policy Q-learning, or on-policy policy gradient methods.
By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods.
Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.

assistant: text: We propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video

Paper content:
We propose a model that is able to perform physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available.
Existing physical scene understanding methods require either object state supervision, or do not integrate with differentiable physics to learn interpretable system parameters and states.
We address this problem through a \textit{physics-as-inverse-graphics} approach that brings together vision-as-inverse-graphics and differentiable physics engines, where objects and explicit state and velocity representations are discovered by the model.
This framework allows us to perform long term extrapolative video prediction, as well as vision-based model-predictive control.
Our approach significantly outperforms related unsupervised methods in long-term future frame prediction of systems with interacting objects (such as ball-spring or 3-body gravitational systems), due to its ability to build dynamics into the model as an inductive bias.
We further show the value of this tight vision-physics integration by demonstrating data-efficient learning of vision-actuated model-based control for a pendulum system.
We also show that the controller's interpretability provides unique capabilities in goal-driven control and physical reasoning for zero-data adaptation.

assistant: text: We propose a model that is able to perform physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fair Resource Allocation in Federated Learning

Paper content:
Federated learning involves jointly learning over massively distributed partitions of data generated on remote devices.
Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices.
In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by resource allocation strategies in wireless networks that encourages a more fair accuracy distribution across devices in federated networks.
To solve q-FFL, we devise a scalable method, q-FedAvg, that can run in federated networks.
We validate both the improved fairness and flexibility of q-FFL and the efficiency of q-FedAvg through simulations on federated datasets.

assistant: text: We propose a novel optimization objective that encourages fairness in heterogeneous federated networks, and develop a scalable method to solve it.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lattice Representation Learning

Paper content:
We introduce the notion of \emph{lattice representation learning}, in which the representation for some object of interest (e.g. a sentence or an image) is a lattice point in an Euclidean space.
Our main contribution is a result for replacing an objective function which employs lattice quantization with an objective function in which quantization is absent, thus allowing optimization techniques based on gradient descent to apply; we call the resulting algorithms \emph{dithered stochastic gradient descent} algorithms as they are designed explicitly to allow for an optimization procedure where only local information is employed.
We also argue that a technique commonly used in Variational Auto-Encoders (Gaussian priors and Gaussian approximate posteriors) is tightly connected with the idea of lattice representations, as the quantization error in good high dimensional lattices can be modeled as a Gaussian distribution.
We use a traditional encoder/decoder architecture to explore the idea of latticed valued representations, and provide experimental evidence of the potential of using lattice representations by modifying the \texttt{OpenNMT-py} generic \texttt{seq2seq} architecture  so that it can implement not only Gaussian dithering of representations, but also the well known straight-through estimator and its application to vector quantization.
The present work is inspired by a belief that information theory, and in particular lossy compression theory can be very effective in serving as a theoretical foundation for problems in representation learning, including the design and analysis of highly performant practical algorithms.
We have introduced lattices as a possible way to create discrete representations, and proved a fundamental result which allows us to train computational networks that use lattice quantized dithering using an equivalent (in an expected sense) computational network which replaces quantization with dithering, thus allowing gradient descent to apply.
This result also allows us to use only local information during the optimization, thus additionally enabling stochastic gradient descent.
We also established a fundamental connection between the use of good high dimensional lattices and the idea of Gaussian dithering, which is common in generative modeling settings such as Variational Autoencoders.
Finally, we provided initial experimental evidence of the potential of using lattices in an VAE setting, where we contrasted the performance of a rectangular lattice based VAE and two types of Gaussian VAEs.
The bottom line is that if one is interested in getting close to the performance of a Gaussian VAE with discrete representations with a good theoretical basis, we suggest the reader to consider lattices and to train them using dithered stochastic gradient descent.

assistant: text: We propose to use lattices to represent objects and prove a fundamental result on how to train networks that use them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

Paper content:
Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a considerable amount of experience to be collected by the agent.
In practical settings, such as robotics, this involves repeatedly attempting a task, resetting the environment between each attempt.
However, not all tasks are easily or automatically reversible.
In practice, this learning process requires considerable human intervention.
In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and backward policy, with the backward policy resetting the environment for a subsequent attempt.
By learning a value function for the backward policy, we can automatically determine when the forward policy is about to enter a non-reversible state, providing for uncertainty-aware safety aborts.
Our experiments illustrate that proper use of the backward policy can greatly reduce the number of manual resets required to learn a task and can reduce the number of unsafe actions that lead to non-reversible states.

assistant: text: We propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and backward policy, with the backward policy resetting the environment for a subsequent attempt.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Paper content:
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks.
EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion.
On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks.
EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data.
We also performed extensive ablation studies and suggest parameters for practical use.

assistant: text: Simple text augmentation techniques can significantly boost performance on text classification tasks, especially for small datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interactive Grounded Language Acquisition and Generalization in a 2D World

Paper content:
We build a virtual agent for learning language in a 2D maze-like world.
The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards.
It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and question answering.
It learns simultaneously the visual representations of the world, the language, and the action control.
By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences.
The new words are transferred from the answers of language prediction.
Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words.
The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences.
In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.

assistant: text: Training an agent in a 2D virtual world for grounded language acquisition and generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension

Paper content:
We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.
DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors.
We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize corresponding answers from the other version.
This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version.
Further, since the two versions have different level of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating background knowledge not available in the given text.
Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences.
Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset).
This opens up several interesting research avenues wherein DuoRC could complement other Reading Comprehension style datasets to explore novel neural approaches for studying language understanding.

assistant: text: We propose DuoRC, a novel dataset for Reading Comprehension (RC) containing 186,089 human-generated QA pairs created from a collection of 7680 pairs of parallel movie plots and introduce a RC task of reading one version of the plot and answering questions created from the other version; thus by design, requiring complex reasoning and deeper language understanding to overcome the poor lexical overlap between the plot and the question.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Memory-Based Graph Networks

Paper content:
Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs.
We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling.
We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory.
The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks.
We also show that the learned representations could correspond to chemical features in the molecule data.

assistant: text: We introduce an efficient memory layer that can learn representation and coarsen input graphs simultaneously without relying on message passing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders

Paper content:
Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming a distribution p(z) over latent space, and an inference model that infers likely latent codes for each data point (Kingma and Welling, 2013).
Recent work shows that traditional training methods tend to yield solutions that violate modeling desiderata: (1) the learned generative model captures the observed data distribution but does so while ignoring the latent codes, resulting in codes that do not represent the data (e.g. van den Oord et al. (2017); Kim et al. (2018)); (2) the aggregate of the learned latent codes does not match the prior p(z).
This mismatch means that the learned generative model will be unable to generate realistic data with samples from p(z)(e.g. Makhzani et al. (2015); Tomczak and Welling (2017)).
In this paper, we demonstrate that both issues stem from the fact that the global optima of the VAE training objective often correspond to undesirable solutions.
Our analysis builds on two observations: (1) the generative model is unidentifiable – there exist many generative models that explain the data equally well, each with different (and potentially unwanted) properties and (2) bias in the VAE objective – the VAE objective may prefer generative models that explain the data poorly but have posteriors that are easy to approximate.
We present a novel inference method, LiBI, mitigating the problems identified in our analysis.
On synthetic datasets, we show that LiBI can learn generative models that capture the data distribution and inference models that better satisfy modeling assumptions when traditional methods struggle to do so.

assistant: text: We characterize problematic global optima of the VAE objective and present a novel inference method to avoid such optima.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Wavelet Pooling for Convolutional Neural Networks

Paper content:
Convolutional Neural Networks continuously advance the progress of 2D and 3D image and object classification.
The steadfast usage of this algorithm requires constant evaluation and upgrading of foundational concepts to maintain progress.
Network regularization techniques typically focus on convolutional layer operations, while leaving pooling layer operations without suitable options.
We introduce Wavelet Pooling as another alternative to traditional neighborhood pooling.
This method decomposes features into a second level decomposition, and discards the first-level subbands to reduce feature dimensions.
This method addresses the overfitting problem encountered by max pooling, while reducing features in a more structurally compact manner than pooling via neighborhood regions.
Experimental results on four benchmark classification datasets demonstrate our proposed method outperforms or performs comparatively with methods like max, mean, mixed, and stochastic pooling.

assistant: text: Pooling is achieved using wavelets instead of traditional neighborhood approaches (max, average, etc).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem

Paper content:
We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP).
We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively.
A graph neural network (GNN) is trained to capture the local and global graph structure and give the prior probability of selecting each vertex every step.
The prior probability provides a heuristics for MCTS, and the MCTS output is an improved probability for selecting the successive vertex, as it is the feedback information by fusing the prior with the scouting procedure.
Experimental results on TSP up to 100 nodes demonstrate that the proposed method obtains shorter tours than other learning-based methods.

assistant: text: A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Selective Autoencoder

Paper content:
Despite promising progress on unimodal data imputation (e.g. image inpainting), models for multimodal data imputation are far from satisfactory.
In this work, we propose variational selective autoencoder (VSAE) for this task.
Learning only from partially-observed data, VSAE can model the joint distribution of observed/unobserved modalities and the imputation mask, resulting in a unified model for various down-stream tasks including data generation and imputation.
Evaluation on synthetic high-dimensional and challenging low-dimensional multimodal datasets shows significant improvement over state-of-the-art imputation models.

assistant: text: We propose a novel VAE-based framework learning from partially-observed data for imputation and generation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ConQUR: Mitigating Delusional Bias in Deep Q-Learning

Paper content:
Delusional bias is a fundamental source of error in approximate Q-learning.
To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates.
In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "consistent" with the underlying greedy policy class.
We introduce a simple penalization scheme that encourages Q-labels used across training batches to remain (jointly) consistent with the expressible policy class.
We also propose a search framework that allows multiple Q-approximators to be generated and tracked, thus mitigating the effect of premature (implicit) policy commitments.
Experimental results demonstrate that these methods can improve the performance of Q-learning in a variety of Atari games, sometimes dramatically.

assistant: text: We developed a search framework and consistency penalty to mitigate delusional bias.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ENRICHMENT OF FEATURES FOR CLASSIFICATION USING AN OPTIMIZED LINEAR/NON-LINEAR COMBINATION OF INPUT FEATURES

Paper content:
Automatic classification of objects is one of the most important tasks in engineering
and data mining applications.
Although using more complex and advanced
classifiers can help to improve the accuracy of classification systems, it can be
done by analyzing data sets and their features for a particular problem.
Feature
combination is the one which can improve the quality of the features.
In this paper,
a structure similar to Feed-Forward Neural Network (FFNN) is used to generate an
optimized linear or non-linear combination of features for classification.
Genetic
Algorithm (GA) is applied to update weights and biases.
Since nature of data sets
and their features impact on the effectiveness of combination and classification
system, linear and non-linear activation functions (or transfer function) are used
to achieve more reliable system.
Experiments of several UCI data sets and using
minimum distance classifier as a simple classifier indicate that proposed linear and
non-linear intelligent FFNN-based feature combination can present more reliable
and promising results.
By using such a feature combination method, there is no
need to use more powerful and complex classifier anymore.

assistant: text: A method for enriching and combining features to improve classification accuracy
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spectral  Embedding of Regularized Block Models

Paper content:
Spectral embedding is a popular technique for the representation of graph data.
Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering.
In this paper, we explain on a simple block model the impact of the complete graph regularization, whereby a constant is added to all entries of the adjacency matrix.
Specifically, we show that the regularization forces the spectral embedding  to focus on  the  largest blocks, making the representation less sensitive to noise or outliers.
We illustrate these results on both  on both synthetic and real data, showing how regularization improves standard clustering scores.

assistant: text: Graph regularization forces spectral embedding to focus on the largest clusters, making the representation less sensitive to noise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware

Paper content:
As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations.
A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the untrusted software stack.
However, these isolation guarantees come at a price in performance, compared to untrusted alternatives.
This paper initiates the study of high performance execution of Deep Neural Networks (DNNs) in TEEs by efficiently partitioning DNN computations between trusted and untrusted devices.
Building upon an efficient outsourcing scheme for matrix multiplication, we propose Slalom, a framework that securely delegates execution of all linear layers in a DNN from a TEE (e.g., Intel SGX or Sanctum) to a faster, yet untrusted, co-located processor.
We evaluate Slalom by running DNNs in an Intel SGX enclave, which selectively delegates work to an untrusted GPU.
For canonical DNNs (VGG16, MobileNet and ResNet variants) we obtain 6x to 20x increases in throughput for verifiable inference, and 4x to 11x for verifiable and private inference.

assistant: text: We accelerate secure DNN inference in trusted execution environments (by a factor 4x-20x) by selectively outsourcing the computation of linear layers to a faster yet untrusted co-processor.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: WSNet: Learning Compact and Efficient Networks with Weight Sampling

Paper content:
We present a new approach and a novel architecture, termed WSNet, for learning compact and efficient deep neural networks.
Existing approaches conventionally learn full model parameters independently and then compress them via \emph{ad hoc} processing such as model pruning or filter factorization.
Alternatively, WSNet proposes learning model parameters by sampling from a compact set of learnable parameters, which naturally enforces {parameter sharing} throughout the learning process.
We demonstrate that such a novel weight sampling approach (and induced WSNet) promotes both weights and computation sharing favorably.
By employing this method, we can more efficiently learn much smaller networks with competitive performance compared to baseline networks with equal numbers of convolution filters.
Specifically, we consider learning compact and efficient 1D convolutional neural networks for audio classification.
Extensive experiments on multiple audio classification datasets verify the effectiveness of WSNet.
Combined with weight quantization, the resulted models are up to \textbf{180$\times$} smaller and theoretically up to \textbf{16$\times$} faster than the well-established baselines, without noticeable performance drop.

assistant: text: We present a novel network architecture for learning compact and efficient deep neural networks