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

Title: Learning Abstract Models for Long-Horizon Exploration

Paper content:
In high-dimensional reinforcement learning settings with sparse rewards, performing
effective exploration to even obtain any reward signal is an open challenge.
While model-based approaches hold promise of better exploration via planning, it
is extremely difficult to learn a reliable enough Markov Decision Process (MDP)
in high dimensions (e.g., over 10^100 states).
In this paper, we propose learning
an abstract MDP over a much smaller number of states (e.g., 10^5), which we can
plan over for effective exploration.
We assume we have an abstraction function
that maps concrete states (e.g., raw pixels) to abstract states (e.g., agent position,
ignoring other objects).
In our approach, a manager maintains an abstract
MDP over a subset of the abstract states, which grows monotonically through targeted
exploration (possible due to the abstract MDP).
Concurrently, we learn a
worker policy to travel between abstract states; the worker deals with the messiness
of concrete states and presents a clean abstraction to the manager.
On three of
the hardest games from the Arcade Learning Environment (Montezuma's,
Pitfall!, and Private Eye), our approach outperforms the previous
state-of-the-art by over a factor of 2 in each game.
In Pitfall!, our approach is
the first to achieve superhuman performance without demonstrations.

assistant: text: We automatically construct and explore a small abstract Markov Decision Process, enabling us to achieve state-of-the-art results on Montezuma's Revenge, Pitfall!, and Private Eye by a significant margin.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SPIGAN: Privileged Adversarial Learning from Simulation

Paper content:
Deep Learning for Computer Vision depends mainly on the source of supervision.
Photo-realistic simulators can generate large-scale automatically labeled synthetic data, but introduce a domain gap negatively impacting performance.
We propose a new unsupervised domain adaptation algorithm, called SPIGAN, relying on Simulator Privileged Information (PI) and Generative Adversarial Networks (GAN).
We use internal data from the simulator as PI during the training of a target task network.
We experimentally evaluate our approach on semantic segmentation.
We train the networks on real-world Cityscapes and Vistas datasets, using only unlabeled real-world images and synthetic labeled data with z-buffer (depth) PI from the SYNTHIA dataset.
Our method improves over no adaptation and state-of-the-art unsupervised domain adaptation techniques.

assistant: text: An unsupervised sim-to-real domain adaptation method for semantic segmentation using privileged information from a simulator with GAN-based image translation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: INVOCMAP: MAPPING METHOD NAMES TO METHOD INVOCATIONS VIA MACHINE LEARNING

Paper content:
Implementing correct method invocation is an important task for software developers.
However, this is challenging work, since the structure of method invocation can be complicated.
In this paper, we propose InvocMap, a code completion tool allows developers to obtain an implementation of multiple method invocations from a list of method names inside code context.
InvocMap is able to predict the nested method invocations which their names didn’t appear in the list of input method names given by developers.
To achieve this, we analyze the Method Invocations by four levels of abstraction.
We build a Machine Translation engine to learn the mapping from the first level to the third level of abstraction of multiple method invocations, which only requires developers to manually add local variables from generated expression to get the final code.
We evaluate our proposed approach on six popular libraries: JDK, Android, GWT, Joda-Time, Hibernate, and Xstream.
With the training corpus of 2.86 million method invocations extracted from 1000 Java Github projects and the testing corpus extracted from 120 online forums code snippets, InvocMap achieves the accuracy rate up to 84 in F1- score depending on how much information of context provided along with method names, that shows its potential for auto code completion.

assistant: text: This paper proposes a theory of classifying Method Invocations by different abstraction levels and conducting a statistical approach for code completion from method name to method invocation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gradient-Based Neural DAG Learning

Paper content:
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.
We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks.
This extension allows to model complex interactions while being more global in its search compared to other greedy approaches.
In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods.
On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks while being competitive with existing greedy search methods on important metrics for causal inference.

assistant: text: We are proposing a new score-based approach to structure/causal learning leveraging neural networks and a recent continuous constrained formulation to this problem
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Recurrent Models for Solving Partially Observable Control Tasks

Paper content:
In partially observable (PO) environments, deep reinforcement learning (RL) agents often suffer from unsatisfactory performance, since two problems need to be tackled together: how to extract information from the raw observations to solve the task, and how to improve the policy.
In this study, we propose an RL algorithm for solving PO tasks.
Our method comprises two parts: a variational recurrent model (VRM) for modeling the environment, and an RL controller that has access to both the environment and the VRM.
The proposed algorithm was tested in two types of PO robotic control tasks, those in which either coordinates or velocities were not observable and those that require long-term memorization.
Our experiments show that the proposed algorithm achieved better data efficiency and/or learned more optimal policy than other alternative approaches in tasks in which unobserved states cannot be inferred from raw observations in a simple manner.

assistant: text: A deep RL algorithm for solving POMDPs by auto-encoding the underlying states using a variational recurrent model
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding

Paper content:
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data.
However, existing graph embedding models either fail to incorporate node attribute information during training or suffer from node attribute noise, which compromises the accuracy.
Moreover, very few of them scale to large graphs due to their high computational complexity and memory usage.
In this paper we propose GraphZoom, a multi-level framework for improving both accuracy and scalability of unsupervised graph embedding algorithms.
GraphZoom first performs graph fusion to generate a new graph that effectively encodes the topology of the original graph and the node attribute information.
This fused graph is then repeatedly coarsened into a much smaller graph by merging nodes with high spectral similarities.
GraphZoom allows any existing embedding methods to be applied to the coarsened graph, before it progressively refine the embeddings obtained at the coarsest level to increasingly finer graphs.
We have evaluated our approach on a number of popular graph datasets for both transductive and inductive tasks.
Our experiments show that GraphZoom increases the classification accuracy and significantly reduces the run time compared to state-of-the-art unsupervised embedding methods.

assistant: text: A multi-level spectral approach to improving the quality and scalability of unsupervised graph embedding.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Evaluation of Neural Sequence Models

Paper content:
We present methodology for using dynamic evaluation to improve neural sequence models.
Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher probabilities to re-occurring sequential patterns.
Dynamic evaluation outperforms existing adaptation approaches in our comparisons.
Dynamic evaluation improves the state-of-the-art word-level perplexities on the Penn Treebank and WikiText-2 datasets to 51.1 and 44.3 respectively, and the state-of-the-art character-level cross-entropies on the text8 and Hutter Prize datasets to 1.19 bits/char and 1.08 bits/char respectively.

assistant: text: Paper presents dynamic evaluation methodology for adaptive sequence modelling
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Compositional Embeddings: Joint Perception and Comparison of Class Label Sets

Paper content:
We explore the idea of compositional set embeddings that can be used to infer not
just a single class, but the set of classes associated with the input data (e.g., image,
video, audio signal).
This can be useful, for example, in multi-object detection in
images, or multi-speaker diarization (one-shot learning) in audio.
In particular, we
devise and implement two novel models consisting of (1) an embedding function
f trained jointly with a “composite” function g that computes set union opera-
tions between the classes encoded in two embedding vectors; and (2) embedding
f trained jointly with a “query” function h that computes whether the classes en-
coded in one embedding subsume the classes encoded in another embedding.
In
contrast to prior work, these models must both perceive the classes associated
with the input examples, and also encode the relationships between different class
label sets.
In experiments conducted on simulated data, OmniGlot, and COCO
datasets, the proposed composite embedding models outperform baselines based
on traditional embedding approaches.

assistant: text: We explored how a novel method of compositional set embeddings can both perceive and represent not just a single class but an entire set of classes that is associated with the input data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes

Paper content:
The extended Kalman filter (EKF) is a classical signal processing algorithm which performs efficient approximate Bayesian inference in non-conjugate models by linearising the local measurement function, avoiding the need to compute intractable integrals when calculating the posterior.
In some cases the EKF outperforms methods which rely on cubature to solve such integrals, especially in time-critical real-world problems.
The drawback of the EKF is its local nature, whereas state-of-the-art methods such as variational inference or expectation propagation (EP) are considered global approximations.
We formulate power EP as a nonlinear Kalman filter, before showing that linearisation results in a globally iterated algorithm that exactly matches the EKF on the first pass through the data, and iteratively improves the linearisation on subsequent passes.
An additional benefit is the ability to calculate the limit as the EP power tends to zero, which removes the instability of the EP-like algorithm.
The resulting inference scheme solves non-conjugate temporal Gaussian process models in linear time, $\mathcal{O}(n)$, and in closed form.

assistant: text: We unify the extended Kalman filter (EKF) and the state space approach to power expectation propagation (PEP) by solving the intractable moment matching integrals in PEP via linearisation. This leads to a globally iterated extension of the EKF.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility

Paper content:
Machine learning (ML) research has investigated prototypes: examples that are representative of the behavior to be learned.
We systematically evaluate five methods for identifying prototypes, both ones previously introduced as well as new ones we propose, finding all of them to provide meaningful but different interpretations.
Through a human study, we confirm that all five metrics are well matched to human intuition.
Examining cases where the metrics disagree offers an informative perspective on the properties of data and algorithms used in learning, with implications for data-corpus construction, efficiency, adversarial robustness, interpretability, and other ML aspects.
In particular, we confirm that the "train on hard" curriculum approach can improve accuracy on many datasets and tasks, but that it is strictly worse when there are many mislabeled or ambiguous examples.

assistant: text: We can identify prototypical and outlier examples in machine learning that are quantifiably very different, and make use of them to improve many aspects of neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Brain-inspired Robust Vision using Convolutional Neural Networks with Feedback

Paper content:
Humans have the remarkable ability to correctly classify images despite possible degradation.
Many studies have suggested that this hallmark of human vision results from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways.
Motivated by such interaction, we propose a new neuro-inspired model, namely Convolutional Neural Networks with Feedback (CNN-F).
CNN-F extends CNN with a feedback generative network, combining bottom-up and top-down inference to perform approximate loopy belief propagation.
We show that CNN-F's iterative inference allows for disentanglement of latent variables across layers.
We validate the advantages of CNN-F over the baseline CNN.
Our experimental results suggest that the CNN-F is more robust to image degradation such as pixel noise, occlusion, and blur.
Furthermore, we show that the CNN-F is capable of restoring original images from the degraded ones with high reconstruction accuracy while introducing negligible artifacts.

assistant: text: CNN-F extends CNN with a feedback generative network for robust vision.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Integration of Background Knowledge in Neural NLU Systems

Paper content:
Common-sense or background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, the requisite background knowledge is indirectly acquired from static corpora.
We develop a new reading architecture for the dynamic integration of explicit background knowledge in NLU models.
A new task-agnostic reading module provides refined word representations to a task-specific NLU architecture by processing background knowledge in the form of free-text statements, together with the task-specific inputs.
Strong performance on the tasks of document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of our approach.
Analysis shows that our models learn to exploit knowledge selectively and in a semantically appropriate way.

assistant: text: In this paper we present a task-agnostic reading architecture for the dynamic integration of explicit background knowledge in neural NLU models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Gradient Descent with Biased but Consistent Gradient Estimators

Paper content:
Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization.
Research on SGD resurged recently in machine learning for optimizing convex loss functions and training nonconvex deep neural networks.
The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization.
There exist, however, many scenarios (e.g., graphs) where an unbiased estimator may be as expensive to compute as the full gradient because training examples are interconnected.
Recently, Chen et al. (2018) proposed using a consistent gradient estimator as an economic alternative.
Encouraged by empirical success, we show, in a general setting, that consistent estimators result in the same convergence behavior as do unbiased ones.
Our analysis covers strongly convex, convex, and nonconvex objectives.
We verify the results with illustrative experiments on synthetic and real-world data.
This work opens several new research directions, including the development of more efficient SGD updates with consistent estimators and the design of efficient training algorithms for large-scale graphs.
Consider the standard setting of supervised learning.
There exists a joint probability distribution P (x, y) of data x and associated label y and the task is to train a predictive model, parameterized by w, that minimizes the expected loss between the prediction and the ground truth y.
Let us organize the random variables as ξ = (x, y) and use the notation (w; ξ) for the loss.
If ξ i = (x i , y i ), i = 1, . . . , n, are iid training examples drawn from P , then the objective function is either one of the following well-known forms: expected risk f (w) = E[ (w; ξ)]; empirical risk f (w) = 1 n n i=1 (w; ξ i ).
Stochastic gradient descent (SGD), which dates back to the seminal work of Robbins & Monro (1951) , has become the de-facto optimization method for solving these problems in machine learning.
In SGD, the model parameter is updated until convergence with the rule
where γ k is a step size and g k is an unbiased estimator of the gradient ∇f (w k ).
Compared with the full gradient (as is used in deterministic gradient descent), an unbiased estimator involves only one or a few training examples ξ i and is usually much more efficient to compute.

assistant: text: Convergence theory for biased (but consistent) gradient estimators in stochastic optimization and application to graph convolutional networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Decompose Compound Questions with Reinforcement Learning

Paper content:
As for knowledge-based question answering, a fundamental problem is to relax the assumption of answerable questions from simple questions to compound questions.
Traditional approaches firstly detect topic entity mentioned in questions, then traverse the knowledge graph to find relations as a multi-hop path to answers, while we propose a novel approach to leverage simple-question answerers to answer compound questions.
Our model consists of two parts:
(i) a novel learning-to-decompose agent that learns a policy to decompose a compound question into simple questions and
(ii) three independent simple-question answerers that classify the corresponding relations for each simple question.
Experiments demonstrate that our model learns complex rules of compositionality as stochastic policy, which benefits simple neural networks to achieve state-of-the-art results on WebQuestions and MetaQA.
We analyze the interpretable decomposition process as well as generated partitions.

assistant: text: We propose a learning-to-decompose agent that helps simple-question answerers to answer compound question over knowledge graph.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AtomNAS: Fine-Grained End-to-End Neural Architecture Search

Paper content:
Designing of search space is a critical problem for neural architecture search (NAS) algorithms.
We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms.
This search space facilitates direct selection of channel numbers and kernel sizes in convolutions.
In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training.
The algorithm is further accelerated by a dynamic network shrinkage technique.
Instead of a  search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner.
Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost.
We open our entire codebase at: https://github.com/meijieru/AtomNAS.

assistant: text: A new state-of-the-art on Imagenet for mobile setting
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Paper content:
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.
In this work, we provide a comprehensive study of the contribution of different components in M-BERT to its cross-lingual ability.
We study the impact of linguistic properties of the languages, the architecture of the model, and of the learning objectives.
The experimental study is done in the context of three typologically different languages -- Spanish, Hindi, and Russian -- and using two conceptually different NLP tasks, textual entailment and named entity recognition.
Among our key conclusions is the fact that lexical overlap between languages plays a negligible role in the cross-lingual success, while the depth of the network is an important part of it

assistant: text: Cross-Lingual Ability of Multilingual BERT: An Empirical Study
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification

Paper content:
We propose a novel subgraph image representation for classification of network fragments with the target being their parent networks.
The graph image representation is based on 2D image embeddings of adjacency matrices.
We use this image representation in two modes.
First, as the input to a machine learning algorithm.
Second, as the input to a pure transfer learner.
Our conclusions from multiple datasets are that
1. deep learning using structured image features performs the best compared to graph kernel and classical features based methods; and,
2. pure transfer learning works effectively with minimum interference from the user and is robust against small data.

assistant: text: We convert subgraphs into structured images and classify them using 1. deep learning and 2. transfer learning (Caffe) and achieve stunning results.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Preconditioner on Matrix Lie Group for SGD

Paper content:
We study two types of preconditioners and preconditioned stochastic gradient descent (SGD) methods in a unified framework.
We call the first one the Newton type due to its close relationship to the Newton method, and the second one the Fisher type as its preconditioner is closely related to the inverse of Fisher information matrix.
Both preconditioners can be derived from one framework, and efficiently estimated on any matrix Lie groups designated by the user using natural or relative gradient descent minimizing certain preconditioner estimation criteria.
Many existing preconditioners and methods, e.g., RMSProp, Adam, KFAC, equilibrated SGD, batch normalization, etc., are special cases of or closely related to either the Newton type or the Fisher type ones.
Experimental results on relatively large scale machine learning  problems are reported for performance study.

assistant: text: We propose a new framework for preconditioner learning, derive new forms of preconditioners and learning methods, and reveal the relationship to methods like RMSProp, Adam, Adagrad, ESGD, KFAC, batch normalization, etc.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using Semantic Distance as a Heuristic for Service Planning

Paper content:
With a growing number of available services, each having slightly different parameters, preconditions and effects, automated planning on general semantic services become highly relevant.
However, most exiting planners only consider PDDL, or if they claim to use OWL-S, they usually translate it to PDDL, losing much of the semantics on the way.
In this paper, we propose a new domain-independent heuristic based on a semantic distance that can be used by generic planning algorithms such as A* for automated planning of semantic services described with OWL-S.
For the heuristic to include more relevant information we calculate the heuristic at runtime.
Using this heuristic, we are able to produce better results (fewer expanded states) in less time than with established techniques.

assistant: text: Describing a semantic heuristics which builds upon an OWL-S service description and uses word and sentence distance measures to evaluate the usefulness of services for a given goal.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Guide Actor-Critic for Continuous Control

Paper content:
Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic.
However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter.
In this paper, we propose a novel actor-critic method called the guide actor-critic (GAC).
GAC firstly learns a guide actor that locally maximizes the critic and then it updates the policy parameter based on the guide actor by supervised learning.
Our main theoretical contributions are two folds.
First, we show that GAC updates the guide actor by performing second-order optimization in the action space where the curvature matrix is based on the Hessians of the critic.
Second, we show that the deterministic policy gradient method is a special case of GAC when the Hessians are ignored.
Through experiments, we show that our method is a promising reinforcement learning method for continuous controls.

assistant: text: This paper proposes a novel actor-critic method that uses Hessians of a critic to update an actor.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Should All Cross-Lingual Embeddings Speak English?

Paper content:
Most of recent work in cross-lingual word embeddings is severely Anglocentric.
The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting.
With this work, however, we challenge these practices.
First, we show that the choice of hub language can significantly impact downstream lexicon induction performance.
Second, we both expand the current evaluation dictionary collection to include all language pairs using triangulation, and also create new dictionaries for under-represented languages.
Evaluating established methods over all these language pairs sheds light into their suitability and presents new challenges for the field.
Finally, in our analysis we identify general guidelines for strong cross-lingual embeddings baselines, based on more than just Anglocentric experiments.

assistant: text: The choice of the hub (target) language affects the quality of cross-lingual embeddings, which shouldn't be evaluated only  on English-centric dictionaries.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

Paper content:
Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images.
In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations.
It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments.
Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization.
We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.

assistant: text: We propose a simple randomization technique for improving generalization in deep reinforcement learning across tasks with various unseen visual patterns.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational Domain Adaptation

Paper content:
This paper proposes variational domain adaptation, a unified, scalable, simple framework for learning multiple distributions through variational inference.
Unlike the existing methods on domain transfer through deep generative models, such as StarGAN (Choi et al., 2017) and UFDN (Liu et al., 2018), the variational domain adaptation has three advantages.
Firstly, the  samples from the target are not required.
Instead, the framework requires one known source as a prior $p(x)$ and binary discriminators, $p(\mathcal{D}_i|x)$, discriminating the target domain $\mathcal{D}_i$ from others.
Consequently, the framework regards a target as a posterior that can be explicitly formulated through the Bayesian inference, $p(x|\mathcal{D}_i) \propto p(\mathcal{D}_i|x)p(x)$, as exhibited by a further proposed model of dual variational autoencoder (DualVAE).
Secondly, the framework is scablable to large-scale domains.
As well as VAE encodes a sample $x$ as a mode on a latent space: $\mu(x) \in \mathcal{Z}$, DualVAE encodes a domain $\mathcal{D}_i$ as a mode on the dual latent space $\mu^*(\mathcal{D}_i) \in \mathcal{Z}^*$, named domain embedding.
It reformulates the posterior with a natural paring $\langle, \rangle: \mathcal{Z} \times \mathcal{Z}^* \rightarrow \Real$, which can be expanded to uncountable infinite domains such as continuous domains as well as interpolation.
Thirdly, DualVAE fastly converges without sophisticated automatic/manual hyperparameter search in comparison to GANs as it requires only one additional parameter to VAE.
Through the numerical experiment, we demonstrate the three benefits with multi-domain image generation task on CelebA with up to 60 domains, and exhibits that DualVAE records the state-of-the-art performance outperforming StarGAN and UFDN.

assistant: text: This paper proposes variational domain adaptation, a uniﬁed, scalable, simple framework for learning multiple distributions through variational inference
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Modeling the Long Term Future in Model-Based Reinforcement Learning

Paper content:
In model-based reinforcement learning, the agent interleaves between model learning and planning.
These two components are  inextricably intertwined.
If the model is not able to provide sensible long-term prediction, the executed planer would exploit model flaws, which can yield catastrophic failures.
This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration.
To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference.
We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions.
Moreover, by planning in the latent space, the planner's solution is ensured to be within regions where the model is valid.
An exploration strategy can be devised by searching for unlikely trajectories under the model.
Our methods achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

assistant: text: incorporating, in the model, latent variables that encode future content improves the long-term prediction accuracy, which is critical for better planning in model-based RL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE

Paper content:
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
discrete or continuous space.
Motivated by the project of design Game AI for King of Glory
(KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
hybrid action space.
To directly apply existing DLR frameworks, existing approaches
either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is
usually less efficient and robust.
In this paper, we propose a parametrized deep Q-network (P-DQN)
for the hybrid action space without approximation or relaxation.
Our algorithm combines DQN and
DDPG and can be viewed as an extension of the DQN to hybrid actions.
The empirical study on the
game KOG validates the efficiency and effectiveness of our method.

assistant: text: A DQN and DDPG hybrid algorithm is proposed to deal with the discrete-continuous hybrid action space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MoET: Interpretable and Verifiable Reinforcement Learning via Mixture of Expert Trees

Paper content:
Deep Reinforcement Learning (DRL) has led to many recent breakthroughs on complex control tasks, such as defeating the best human player in the game of Go.
However, decisions made by the DRL agent are not explainable, hindering its applicability in safety-critical settings.
Viper, a recently proposed technique, constructs a decision tree policy by mimicking the DRL agent.
Decision trees are interpretable as each action made can be traced back to the decision rule path that lead to it.
However, one global decision tree approximating the DRL policy has significant limitations with respect to the geometry of decision boundaries.
We propose MoET, a more expressive, yet still interpretable model based on Mixture of Experts, consisting of a gating function that partitions the state space, and multiple decision tree experts that specialize on different partitions.
We propose a training procedure to support non-differentiable decision tree experts and integrate it into imitation learning procedure of Viper.
We evaluate our algorithm on four OpenAI gym environments, and show that the policy constructed in such a way is more performant and better mimics the DRL agent by lowering mispredictions and increasing the reward.
We also show that MoET policies are amenable for verification using off-the-shelf automated theorem provers such as Z3.

assistant: text: Explainable reinforcement learning model using novel combination of mixture of experts with non-differentiable decision tree experts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Distributional Inclusion Vector Embedding for Unsupervised Hypernymy Detection

Paper content:
Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering.
Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from unlabeled text.
Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy.
This paper introduces {\it distributional inclusion vector embedding (DIVE)}, a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings which preserve the inclusion property of word contexts.
In experimental evaluations more comprehensive than any previous literature of which we are aware---evaluating on 11 datasets using multiple existing as well as newly proposed scoring functions---we find that our method provides up to double the precision of previous unsupervised methods, and the highest average performance, using a much more compact word representation, and yielding many new state-of-the-art results.
In addition, the meaning of each dimension in DIVE is interpretable, which leads to a novel approach on word sense disambiguation as another promising application of DIVE.

assistant: text: We propose a novel unsupervised word embedding which preserves the inclusion property in the context distribution and achieve state-of-the-art results on unsupervised hypernymy detection
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-Mention Learning for Reading Comprehension with Neural Cascades

Paper content:
Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur.
Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via truncation or other means), and carefully searching for the answer within that passage.
However, in some cases, this strategy can be suboptimal,  since by focusing on a specific passage, it becomes difficult to leverage multiple mentions of the same answer throughout the document.
In this work, we take a different approach by constructing lightweight models that are combined in a cascade to find the answer.
Each submodel consists only of feed-forward networks equipped with an attention mechanism, making it trivially parallelizable.
We show that our approach can scale to approximately an order of magnitude larger evidence documents and can aggregate information from multiple mentions of each answer candidate across the document.
Empirically, our approach achieves state-of-the-art performance on both the Wikipedia and web domains of the TriviaQA dataset, outperforming more complex, recurrent architectures.

assistant: text: We propose neural cascades, a simple and trivially parallelizable approach to reading comprehension, consisting only of feed-forward nets and attention that achieves state-of-the-art performance on the TriviaQA dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Repurposing Decoder-Transformer Language Models for Abstractive Summarization

Paper content:
Neural network models have shown excellent fluency and performance when applied to abstractive summarization.
Many approaches to neural abstractive summarization involve the introduction of significant inductive bias, such as pointer-generator architectures, coverage, and partially extractive procedures, designed to mimic human summarization.
We show that it is possible to attain competitive performance by instead directly viewing summarization as language modeling.
We introduce a simple procedure built upon pre-trained decoder-transformers to obtain competitive ROUGE scores using a language modeling loss alone, with no beam-search or other decoding-time optimization, and instead rely on efficient nucleus sampling and greedy decoding.

assistant: text: We introduce a simple procedure to repurpose pre-trained transformer-based language models to perform abstractive summarization well.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bandlimiting Neural Networks Against Adversarial Attacks

Paper content:
In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis.
We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural networks.
We then demonstrate that the vulnerability of neural networks towards adversarial samples can be attributed to these insignificant but non-zero high frequency components.
Based on this analysis, we propose to use a simple post-averaging technique to smooth out these high frequency components to improve the robustness of neural networks against adversarial attacks.
Experimental results on the ImageNet and the CIFAR-10 datasets have shown that our proposed method is universally effective to defend many existing adversarial attacking methods proposed in the literature, including FGSM, PGD, DeepFool and C&W attacks.
Our post-averaging method is simple since it does not require any re-training, and meanwhile it can successfully defend over 80-96% of the adversarial samples generated by these methods without introducing significant performance degradation (less than 2%) on the original clean images.

assistant: text: An insight into the reason of adversarial vulnerability, an effective defense method against adversarial attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic algorithms under single spiked models

Paper content:
We study SGD and Adam for estimating a rank one signal planted in matrix or tensor noise.
The extreme simplicity of the problem setup allows us to isolate the effects of various factors: signal to noise ratio, density of critical points, stochasticity and initialization.
We observe a surprising phenomenon: Adam seems to get stuck in local minima as soon as polynomially many critical points appear (matrix case), while SGD escapes those.
However, when the number of critical points degenerates to exponentials (tensor case), then both algorithms get trapped.
Theory tells us that at fixed SNR the problem becomes intractable for large $d$ and in our experiments SGD does not escape this.
We exhibit the benefits of warm starting in those situations.
We conclude that in this class of problems, warm starting cannot be replaced by stochasticity in gradients to find the basin of attraction.

assistant: text: SGD and Adam under single spiked model for tensor PCA
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Distributed optimization is essential for training large models on large datasets.
Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decouple communications among workers.
Although these methods run faster than AllReduce-based methods, which use blocking communication before every update, the resulting models may be less accurate after the same number of updates.
Inspired by the BMUF method of Chen & Huo (2016), we propose a slow momentum (SloMo) framework, where workers periodically synchronize and perform a momentum update, after multiple iterations of a base optimization algorithm.
Experiments on image classification and machine translation tasks demonstrate that SloMo consistently yields improvements in optimization and generalization performance relative to the base optimizer, even when the additional overhead is amortized over many updates so that the SloMo runtime is on par with that of the base optimizer.
We provide theoretical convergence guarantees showing that SloMo converges to a stationary point of smooth non-convex losses.
Since BMUF is a particular instance of the SloMo framework, our results also correspond to the first theoretical convergence guarantees for BMUF.

assistant: text: SlowMo improves the optimization and generalization performance of communication-efficient decentralized algorithms without sacrificing speed.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Binarizing Networks

Paper content:
We present a method to train self-binarizing neural networks, that is, networks that evolve their weights and activations during training to become binary.
To obtain similar binary networks, existing methods rely on the sign activation function.
This function, however, has no gradients for non-zero values, which makes standard backpropagation impossible.
To circumvent the difficulty of training a network relying on the sign activation function, these methods alternate between floating-point and binary representations of the network during training, which is sub-optimal and inefficient.
We approach the binarization task by training on a unique representation involving a smooth activation function, which is iteratively sharpened during training until it becomes a binary representation equivalent to the sign activation function.
Additionally, we introduce a new technique to perform binary batch normalization that simplifies the conventional batch normalization by transforming it into a simple comparison operation.
This is unlike existing methods, which are forced to the retain the conventional floating-point-based batch normalization.
Our binary networks, apart from displaying advantages of lower memory and computation as compared to conventional floating-point and binary networks, also show higher classification accuracy than existing state-of-the-art methods on multiple benchmark datasets.

assistant: text: A method to binarize both weights and activations of a deep neural network that is efficient in computation and memory usage and performs better than the state-of-the-art.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discovery of Predictive Representations With a Network of General Value Functions

Paper content:
The ability of an agent to {\em discover} its own learning objectives has long been considered a key ingredient for artificial general intelligence.
Breakthroughs in autonomous decision making and reinforcement learning have primarily been in domains where the agent's goal is outlined and clear: such as playing a game to win, or driving safely.
Several studies have demonstrated that learning extramural sub-tasks and auxiliary predictions can improve (1) single human-specified task learning, (2) transfer of learning, (3) and the agent's learned representation of the world.
In all these examples, the agent was instructed what to learn about.
We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent's representation of the world.
Specifically, our system maintains a large collection of predictions, continually pruning and replacing predictions.
We highlight the importance of considering stability rather than convergence for such a system, and develop an adaptive, regularized algorithm towards that aim.
We provide several experiments in computational micro-worlds demonstrating that this simple approach can be effective for discovering useful predictions autonomously.

assistant: text: We investigate a framework for discovery: curating a large collection of predictions, which are used to construct the agent’s representation in partially observable domains.