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

Title: Optimistic Exploration even with a Pessimistic Initialisation

Paper content:
Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL).
In the tabular case, all provably efficient model-free algorithms rely on it.
However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms.
In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation.
Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration.
We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network.
We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.
Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping.
We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.

assistant: text: We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spontaneous Symmetry Breaking in Deep Neural Networks

Paper content:
We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory.
Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights.
Using a two parameter field theory, we find that the network can break such symmetries itself towards the end of training in a process commonly known in physics as spontaneous symmetry breaking.
This corresponds to a network generalizing itself without any user input layers to break the symmetry, but by communication with adjacent layers.
In the layer decoupling limit applicable to residual networks (He et al., 2015), we show that the remnant symmetries that survive the non-linear layers are spontaneously broken based on empirical results.
The Lagrangian for the non-linear and weight layers together has striking similarities with the one in quantum field theory of a scalar.
Using results from quantum field theory we show that our framework is able to explain many experimentally observed phenomena, such as training on random labels with zero error (Zhang et al., 2017), the information bottleneck and the phase transition out of it (Shwartz-Ziv & Tishby, 2017), shattered gradients (Balduzzi et al., 2017), and many more.

assistant: text: Closed form results for deep learning in the layer decoupling limit applicable to Residual Networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sparse and Structured Visual Attention

Paper content:
Visual attention mechanisms have been widely used in image captioning models.
In this paper, to better link the image structure with the generated text, we replace the traditional softmax attention mechanism by two alternative sparsity-promoting transformations: sparsemax and Total-Variation Sparse Attention (TVmax).
With sparsemax, we obtain sparse attention weights, selecting relevant features.
In order to promote sparsity and encourage fusing of the related adjacent spatial locations, we propose TVmax.
By selecting relevant groups of features, the TVmax transformation improves interpretability.
We present results in the Microsoft COCO and Flickr30k datasets, obtaining gains in comparison to softmax.
TVmax outperforms the other compared attention mechanisms in terms of human-rated caption quality and attention relevance.

assistant: text: We propose a new sparse and structured attention mechanism, TVmax, which promotes sparsity and encourages the weight of related adjacent locations to be the same.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Active Multitask Learning with Committees

Paper content:
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.
The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks.
To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks.
The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks.
Our approach reduces the number of queries needed during training while maintaining high accuracy on test data.
Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests.

assistant: text: We propose an active multitask learning algorithm that achieves knowledge transfer between tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?

Paper content:
Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work.
But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations.
In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection, the human interpretation of activation maximization (AM) images, and standard signal-detection measures.
We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates.
Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors.
We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks.
In order to generalize these results, we compared selectivity measures on a few units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'.
Again, we find poor hit-rates and high false-alarm rates for object classification.

assistant: text: Looking for object detectors using many different selectivity measures; CNNs are slightly selective , but not enough to be termed object detectors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Graph Convolution Filters from Data Manifold

Paper content:
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.
Recently, there has been an increasing interest in extending CNNs to the general spatial domain.
Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood.
In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation.
Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.

assistant: text: We devise a novel Depthwise Separable Graph Convolution (DSGC) for the generic spatial domain data, which is highly compatible with depthwise separable convolution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Harmonic Structure-Based Neural Network Model for Musical Pitch Detection

Paper content:
In this paper we design a harmonic acoustic model for pitch detection.
This model arranges conventional convolution and sparse convolution in a way such that the global harmonic patterns captured by sparse convolution are composed of the enough number of local patterns captured by layers of conventional convolution.
When trained on the MAPS dataset, the harmonic model outperforms all existing pitch detection systems trained on the same dataset.
Most impressively, when trained on MAPS with simple data augmentation, the harmonic model with an LSTM layer on top surpasses an up-to-date, more complex pitch detection system trained on the MAESTRO dataset to which complicated data augmentation is applied and whose training split is an order-of-magnitude larger than the training split of MAPS.
The harmonic model has demonstrated potential to be used for advanced automatic music transcription (AMT) systems.
In this paper we designed a harmonic acoustic model for pitch detection.
This model effectively captures the complex frequency interactions characterizing polyphonic pitched music through conventional convolution and sparse convolution inspired by the harmonic structure of pitched music.
In its pure form without RNN and data augmentation, the harmonic model outperformed most of the existing pitch detection systems.
Most noticeably, when trained on MAPS and data augmentation is done, the harmonic model with an LSTM layer on top outdid the complex system in Hawthorne et al. (2019) trained on MAESTRO whose training split 15 times as large as the training split of MAPS.
Thus, the harmonic model has shown great potential to be used for building advanced AMT systems.
A possible future direction is to make more potential of complex spectrograms, instead of using only amplitude spectrograms.
A mixture of signal can be inseparable in the real number domain but could be separable in the complex number domain.
Trabelsi et al. (2018) has done some preliminary study in this direction.
However, our own study showed that the technique of deep complex network proposed in Trabelsi et al. (2018) did not yield a performance comparable with that of real networks.
Therefore, definitely more can be done.

assistant: text: harmonic acoustic model
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Multivariate Mixture of Gaussians for Object Detection under Occlusion

Paper content:
In this paper, we consider the problem of detecting object under occlusion.
Most object detectors formulate bounding box regression as a unimodal task (i.e., regressing a single set of bounding box coordinates independently).
However, we observe that the bounding box borders of an occluded object can have multiple plausible configurations.
Also, the occluded bounding box borders have correlations with visible ones.
Motivated by these two observations, we propose a deep multivariate mixture of Gaussians model for bounding box regression under occlusion.
The mixture components potentially learn different configurations of an occluded part, and the covariances between variates help to learn the relationship between the occluded parts and the visible ones.
Quantitatively, our model improves the AP of the baselines by 3.9% and 1.2% on CrowdHuman and MS-COCO respectively with almost no computational or memory overhead.
Qualitatively, our model enjoys explainability since we can interpret the resulting bounding boxes via the covariance matrices and the mixture components.
We propose a multivariate mixture of Gaussians model for object detection under occlusion.
Quantitatively, it demonstrates consistent improvements over the baselines among MS-COCO, PASCAL VOC 2007, CrowdHuman, and VehicleOcclusion.
Qualitatively, our model enjoys explainability as the detection results can be diagnosed via the covariance matrices and the mixture components.

assistant: text: a deep multivariate mixture of Gaussians model for bounding box regression under occlusion
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing.
Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet.
Further study for demystifying this improvement shows that the transfer learning produces a highly compressible network, which was not the case for the network learned from scratch.
The experimental result shows that there is a negligible accuracy drop in the network learned by transfer learning until it is compressed to 1/128 reduction of the number of convolution ﬁlters.
This result is contrary to the compression without transfer learning which loses more than 5% accuracy at the same compression rate.
result in [2] , the network trained from scratch can also achieve 99.78% accuracy with the extensively 23 augmented data and a long period of training.
Surprisingly, however, although the performance of 24 both networks is similar, the network trained from scratch learns much denser features of the input 25 data than the network trained by transfer learning.
We experimentally show that using standard 26 teacher-student model compression technique [3], the network trained by transfer learning can be 27 compressed to 1/128 reduction of the number of convolution filters with a negligible accuracy drop.
In contrast, the network trained from scratch loses more than 5% accuracy at the same compression 29 rate.
Previous works on the network compression have focused on the methods of compression
[3, 5, 6, 9], but our work is the first report to the best of our knowledge that the transfer learning is 31 related to the network compression.
The rest of the paper is organized as follows.
In Section 2, we explain the method of experiments and
show the compression result in Section 2.3.
Finally, we conclude with a brief discussion in Section 3.
a given input image, we can see that the TL teacher network learns much sparser features of the 64 input data than the Scratch teacher network (Figure 2 ).
The sparsity of activation in the TL teacher

assistant: text: We experimentally show that transfer learning makes sparse features in the network and thereby produces a more compressible network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Evolution of Eigenvalue Decay in Deep Networks

Paper content:
The linear transformations in converged deep networks show fast eigenvalue decay.
The distribution of eigenvalues looks like a Heavy-tail distribution, where the vast majority of eigenvalues is small, but not actually zero, and only a few spikes of large eigenvalues exist.
We use a stochastic approximator to generate histograms of eigenvalues.
This allows us to investigate layers with hundreds of thousands of dimensions.
We show how the distributions change over the course of image net training, converging to a similar heavy-tail spectrum across all intermediate layers.
Understanding the structure in the linear transformations might be an important aspect of understanding generalization in deep networks.
To this end we have presented a stochastic approach that allows us to estimate the eigenvalue spectrum of these transformations.
We show how the spectrum evolves during imagenet training using convolutional networks, more specifically squeeze_net networks.In the future we want to apply similar approaches to estimating the covariance structure of the intermediate feature representations and investigate the relations between covariance matrices and parameter matrices.
Since the estimator we use is differentiable BID5 BID0 , it may be interesting to investigate its usefulness for regularization.

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

Title: (How) Can AI Bots Lie?

Paper content:
Recent work on explanation generation for decision-making problems has viewed the explanation process as one of model reconciliation where an AI agent brings the human mental model (of its capabilities, beliefs, and goals) to the same page with regards to a task at hand.
This formulation succinctly captures many possible types of explanations, as well as explicitly addresses the various properties -- e.g. the social aspects, contrastiveness, and selectiveness -- of explanations studied in social sciences among human-human interactions.
However, it turns out that the same process can be hijacked into producing "alternative explanations" -- i.e. explanations that are not true but still satisfy all the properties of a proper explanation.
In previous work, we have looked at how such explanations may be perceived by the human in the loop and alluded to one possible way of generating them.
In this paper, we go into more details of this curious feature of the model reconciliation process and discuss similar implications to the overall notion of explainable decision-making.

assistant: text: Model Reconciliation is an established framework for plan explanations, but can be easily hijacked to produce lies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: How Aggressive Can Adversarial Attacks Be: Learning Ordered Top-k Attacks

Paper content:
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks.
This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks.
We propose to learn ordered Top-k attacks (k>=1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive).
Two methods are presented.
First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline.
Second, we present an adversarial distillation framework consisting of two components:
(i) Computing an adversarial probability distribution for any given ordered Top-$k$ targeted labels.
(ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty.
In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks.
In experiments, we test Top-k (k=1,2,5,10) attacks in the ImageNet-1000 val dataset using two popular DNNs trained with the clean ImageNet-1000  train dataset, ResNet-50 and DenseNet-121.
Overall, the adversarial distillation approach obtains the best results, especially by large margin when computation budget is limited..
It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by large margin against the modified C&W method for k=10.

assistant: text: ordered Top-k adversarial attacks
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.

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: Evaluating biological plausibility of learning algorithms the lazy way

Paper content:
To which extent can successful machine learning inform our understanding of biological learning?
One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation.
Here we focus on learning in recurrent networks and investigate a range of learning algorithms.
Our approach decomposes them into their computational building blocks and discusses their abstract potential as biological operations.
This alternative strategy provides a “lazy” but principled way of evaluating ML ideas in terms of their biological plausibility

assistant: text: We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Overlapping Community Detection with Graph Neural Networks

Paper content:
Community detection in graphs is of central importance in graph mining, machine learning and network science.
Detecting overlapping communities is especially challenging, and remains an open problem
.  Motivated by the success of graph-based  deep  learning  in  other  graph-related  tasks,  we  study  the  applicability  of this framework for overlapping community detection
. We propose a probabilistic model for overlapping community detection based on the graph neural network architecture
.  Despite its simplicity, our model outperforms the existing approaches in the community recovery task by a large margin
.  Moreover, due to the inductive formulation, the proposed model is able to perform out-of-sample community detection for nodes that were not present at training time

assistant: text: Detecting overlapping communities in graphs using graph neural networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Geometry of Deep Convolutional Networks

Paper content:
We give a formal procedure for computing preimages of convolutional
  network outputs using the dual basis defined from the set of
  hyperplanes associated with the layers of the network.
We point out
  the special symmetry associated with arrangements of hyperplanes of
  convolutional networks that take the form of regular
  multidimensional polyhedral cones.
We discuss  the efficiency of of
  large number of layers of nested cones that result from incremental
  small size convolutions in order to give a good compromise between
  efficient contraction of data to low dimensions and shaping of
  preimage manifolds.
We demonstrate how a specific network flattens a
  non linear input manifold to an affine output manifold and discuss
  it's relevance to understanding classification properties of deep
  networks.

assistant: text: Analysis of deep convolutional networks in terms of associated arrangement of hyperplanes
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning

Paper content:
While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints.
In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior.
We reformulate the problem of IRL on Markov Decision Processes (MDPs) such that, given a nominal model of the environment and a nominal reward function, we seek to estimate state, action, and feature constraints in the environment that motivate an agent’s behavior.
Our approach is based on the Maximum Entropy IRL framework, which allows us to reason about the likelihood of an expert agent’s demonstrations given our knowledge of an MDP.
Using our method, we can infer which constraints can be added to the MDP to most increase the likelihood of observing these demonstrations.
We present an algorithm which iteratively infers the Maximum Likelihood Constraint to best explain observed behavior, and we evaluate its efficacy using both simulated behavior and recorded data of humans navigating around an obstacle.

assistant: text: Our method infers constraints on task execution by leveraging the principle of maximum entropy to quantify how demonstrations differ from expected, un-constrained behavior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method

Paper content:
In recent years, three-dimensional convolutional neural network (3D CNN) are intensively applied in the video analysis and action recognition and receives good performance.
However, 3D CNN leads to massive computation and storage consumption, which hinders its deployment on mobile and embedded devices.
In this paper, we propose a three-dimensional regularization-based pruning method to assign different regularization parameters to different weight groups based on their importance to the network.
Our experiments show that the proposed method outperforms other popular methods in this area.
In recent years, convolutional neural network (CNN) has developed rapidly and has achieved remarkable success in computer vision tasks such as identification, classification and segmentation.
However, due to the lack of motion modeling, this image-based end-to-end feature can not directly apply to videos.
In BID0 BID1 , the authors use three-dimensional convolutional networks (3D CNN) to identify human actions in videos.
Tran et al. proposed a 3D CNN for action recognition which contains 1.75 million parameters BID2 .
The development of 3D CNN also brings challenges because of its higher dimensions.
This leads to massive computing and storage consumption, which hinders its deployment on mobile and embedded devices.In order to reduce the computation cost, researchers propose methods to compress CNN models, including knowledge distillation BID3 , parameter quantization BID4 BID5 , matrix decomposition BID6 and parameter pruning BID7 .
However, all of the above methods are based on two-dimensional convolution.
In this paper, we expand the idea of BID8 to 3D CNN acceleration.
The main idea is to add group regularization items to the objective function and prune weight groups gradually, where the regularization parameters for different weight groups are differently assigned according to some importance criteria.
In this paper, we implement the regularization based method for 3D CNN acceleration.
By assigning different regularization parameters to different weight groups according to the importance estimation, we gradually prune weight groups in the network.
The proposed method achieves better performance than other two popular methods in this area.

assistant: text: In this paper, we propose a three-dimensional regularization-based pruning method to accelerate the 3D-CNN.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Describe Scenes with Programs

Paper content:
Human scene perception goes beyond recognizing a collection of objects and their pairwise relations.
We understand higher-level, abstract regularities within the scene such as symmetry and repetition.
Current vision recognition modules and scene representations fall short in this dimension.
In this paper, we present scene programs, representing a scene via a symbolic program for its objects, attributes, and their relations.
We also propose a model that infers such scene programs by exploiting a hierarchical, object-based scene representation.
Experiments demonstrate that our model works well on synthetic data and transfers to real images with such compositional structure.
The use of scene programs has enabled a number of applications, such as complex visual analogy-making and scene extrapolation.

assistant: text: We present scene programs, a structured scene representation that captures both low-level object appearance and high-level regularity in the scene.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving GANs Using Optimal Transport

Paper content:
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution.
This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance defined in an adversarially learned feature space, resulting in a highly discriminative distance function with unbiased mini-batch gradients.
Experimentally we show OT-GAN to be highly stable when trained with large mini-batches, and we present state-of-the-art results on several popular benchmark problems for image generation.

assistant: text: An extension of GANs combining optimal transport in primal form with an energy distance defined in an adversarially learned feature space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-scale Attributed Node Embedding

Paper content:
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.
Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE).
Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes.
We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings.
Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.

assistant: text: We develop efficient multi-scale approximate attributed network embedding procedures with provable properties.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Probabilistic Planning with Sequential Monte Carlo methods

Paper content:
In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal trajectories.
This enables us to use sampling methods, and thus, tackle planning in continuous domains using a fixed computational budget.
We design a new algorithm,  Sequential Monte Carlo Planning, by leveraging classical methods in Sequential Monte Carlo and Bayesian smoothing in the context of control as inference.
Furthermore, we show that Sequential Monte Carlo Planning can capture multimodal policies and can quickly learn continuous control tasks.

assistant: text: Leveraging control as inference and Sequential Monte Carlo methods, we proposed a probabilistic planning algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Interpretability Evaluation Framework for Deep Neural Networks

Paper content:
Deep neural networks (DNNs) have attained surprising achievement during the last decade due to the advantages of automatic feature learning and freedom of expressiveness.
However,  their interpretability remains mysterious because DNNs are complex combinations of linear and nonlinear transformations.
Even though many models have been proposed to explore the interpretability of DNNs,  several challenges remain unsolved:
1) The lack of interpretability quantity measures for DNNs,  2) the lack of theory for stability of DNNs, and
3) the difficulty to solve nonconvex DNN problems with interpretability constraints.
To address these challenges simultaneously, this paper presents a novel intrinsic interpretability evaluation framework for DNNs.
Specifically, Four independent properties of interpretability are defined based on existing works.
Moreover, we investigate the theory for the stability of DNNs, which is an important aspect of interpretability, and prove that DNNs are generally stable given different activation functions.
Finally, an extended version of deep learning Alternating Direction Method of Multipliers (dlADMM) are proposed to solve DNN problems with interpretability constraints efficiently and accurately.
Extensive experiments on several benchmark datasets validate several DNNs by our proposed interpretability framework.

assistant: text: We propose a novel framework to evaluate the interpretability of neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Paper content:
Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success.
However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend.
In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank.
We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.
Our model's training time is on par or faster and its number of parameters on par or lower than previous models.
It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network.
We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models.
Our implementation is available online.

assistant: text: Personalized propagation of neural predictions (PPNP) improves graph neural networks by separating them into prediction and propagation via personalized PageRank.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Generalization-Stability Tradeoff in Neural Network Pruning

Paper content:
Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting.
This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts.
To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning's effect on generalization relies more on the instability it generates (defined as the drops in test accuracy immediately following pruning) than on the final size of the pruned model.
We demonstrate that even the pruning of unimportant parameters can lead to such instability, and show similarities between pruning and regularizing by injecting noise, suggesting a mechanism for pruning-based generalization improvements that is compatible with the strong generalization recently observed in over-parameterized networks.

assistant: text: We demonstrate that pruning methods which introduce greater instability into the loss also confer improved generalization, and explore the mechanisms underlying this effect.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to cluster in order to transfer across domains and tasks

Paper content:
This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster.
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not (pairwise semantic similarity).
This similarity is category-agnostic and can be learned from data in the source domain using a similarity network.
We then present two novel approaches for performing transfer learning using this similarity function.
First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs.
Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network.
Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches.
Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet.
Our results show that we can reconstruct semantic clusters with high accuracy.
We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets.
Our approach doesn't explicitly deal with domain discrepancy.
If we combine with a domain adaptation loss, it shows further improvement.

assistant: text: A learnable clustering objective to facilitate transfer learning across domains and tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Link Prediction in Hypergraphs using Graph Convolutional Networks

Paper content:
Link prediction in simple graphs is a fundamental problem in which new links between nodes are predicted based on the observed structure of the graph.
However, in many real-world applications, there is a need to model relationships among nodes which go beyond pairwise associations.
For example, in a chemical reaction, relationship among the reactants and products is inherently higher-order.
Additionally, there is need to represent the direction from reactants to products.
Hypergraphs provide a natural way to represent such complex higher-order relationships.
Even though Graph Convolutional Networks (GCN) have recently emerged as a powerful deep learning-based approach for link prediction over simple graphs, their suitability for link prediction in hypergraphs is unexplored -- we fill this gap in this paper and propose Neural Hyperlink Predictor (NHP). NHP adapts GCNs for link prediction in hypergraphs. We propose two variants of NHP --NHP-U and NHP-D -- for link prediction over undirected and directed hypergraphs, respectively.
To the best of our knowledge, NHP-D is the first method for link prediction over directed hypergraphs.
Through extensive experiments on multiple real-world datasets, we show NHP's effectiveness.

assistant: text: We propose Neural Hyperlink Predictor (NHP). NHP adapts graph convolutional networks for link prediction in hypergraphs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Input Alignment along Chaotic directions increases Stability in Recurrent Neural Networks

Paper content:
Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations.
However, it remains unclear how neural circuits encode complex spatio-temporal patterns.
We show that neural dynamics are strongly influenced by the phase alignment between the input and the spontaneous chaotic activity.
Input alignment along the dominant chaotic projections causes the chaotic trajectories to become stable channels (or attractors), hence, improving the computational capability of a recurrent network.
Using mean field analysis, we derive the impact of input alignment on the overall stability of attractors formed.
Our results indicate that input alignment determines the extent of intrinsic noise suppression and hence, alters the attractor state stability, thereby controlling the network's inference ability.

assistant: text: Input Structuring along Chaos for Stability
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Maintaining cooperation in complex social dilemmas using deep reinforcement learning

Paper content:
Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare.
Building artificially intelligent agents that achieve good outcomes in these situations is important because many real world interactions include a tension between selfish interests and the welfare of others.
We show how to modify modern reinforcement learning methods to construct agents that act in ways that are simple to understand, nice (begin by cooperating), provokable (try to avoid being exploited), and forgiving (try to return to mutual cooperation).
We show both theoretically and experimentally that such agents can maintain cooperation in Markov social dilemmas.
Our construction does not require training methods beyond a modification of self-play, thus if an environment is such that good strategies can be constructed in the zero-sum case (eg. Atari) then we can construct agents that solve social dilemmas in this environment.

assistant: text: How can we build artificial agents that solve social dilemmas (situations where individuals face a temptation to increase their payoffs at a cost to total welfare)?
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.

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: Few-shot Text Classification with Distributional Signatures

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: Classification from Positive, Unlabeled and Biased Negative Data

Paper content:
Positive-unlabeled (PU) learning addresses the problem of learning a binary classifier from positive (P) and unlabeled (U) data.
It is often applied to situations where negative (N) data are difficult to be fully labeled.
However, collecting a non-representative N set that contains only a small portion of all possible N data can be much easier in many practical situations.
This paper studies a novel classification framework which incorporates such biased N (bN) data in PU learning.
The fact that the training N data are biased also makes our work very different from those of standard semi-supervised learning.
We provide an empirical risk minimization-based method to address this PUbN classification problem.
Our approach can be regarded as a variant of traditional example-reweighting algorithms, with the weight of each example computed through a preliminary step that draws inspiration from PU learning.
We also derive an estimation error bound for the proposed method.
Experimental results demonstrate the effectiveness of our algorithm in not only PUbN learning scenarios but also ordinary PU leaning scenarios on several benchmark datasets.

assistant: text: This paper studied the PUbN classification problem, where we incorporate biased negative (bN) data, i.e., negative data that is not fully representative of the true underlying negative distribution, into positive-unlabeled (PU) learning.