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

Title: Domain Adaptation for Structured Output via Disentangled Patch Representations

Paper content:
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn strong supervised models like convolutional neural networks.
However, these models trained on one data domain may not generalize well to other domains unequipped with annotations for model finetuning.
To avoid the labor-intensive process of annotation, we develop a domain adaptation method to adapt the source data to the unlabeled target domain.
To this end, we propose to learn discriminative feature representations of patches based on label histograms in the source domain, through the construction of a disentangled space.
With such representations as guidance, we then use an adversarial learning scheme to push the feature representations in target patches to the closer distributions in source ones.
In addition, we show that our framework can integrate a global alignment process with the proposed patch-level alignment and achieve state-of-the-art performance on semantic segmentation.
Extensive ablation studies and experiments are conducted on numerous benchmark datasets with various settings, such as synthetic-to-real and cross-city scenarios.

assistant: text: A domain adaptation method for structured output via learning patch-level discriminative feature representations
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exploring the Hidden Dimension in Accelerating Convolutional Neural Networks

Paper content:
DeePa is a deep learning framework that explores parallelism in all parallelizable dimensions to accelerate the training process of convolutional neural networks.
DeePa optimizes parallelism at the granularity of each individual layer in the network.
We present an elimination-based algorithm that finds an optimal parallelism configuration for every layer.
Our evaluation shows that DeePa achieves up to 6.5× speedup compared to state-of-the-art deep learning frameworks and reduces data transfers by up to 23×.

assistant: text: To the best of our knowledge, DeePa is the first deep learning framework that controls and optimizes the parallelism of CNNs in all parallelizable dimensions at the granularity of each layer.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hyperbolic Discounting and Learning Over Multiple Horizons

Paper content:
Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process.
The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation.
However, evidence from psychology, economics and neuroscience suggests that humans and animals instead have hyperbolic time-preferences.
Here we extend earlier work of Kurth-Nelson and Redish and propose an efficient deep reinforcement learning agent that acts via hyperbolic discounting and other non-exponential discount mechanisms.
We demonstrate that a simple approach approximates hyperbolic discount functions while still using familiar temporal-difference learning techniques in RL.
Additionally, and independent of hyperbolic discounting, we make a surprising discovery that simultaneously learning value functions over multiple time-horizons is an effective auxiliary task which often improves over state-of-the-art methods.

assistant: text: A deep RL agent that learns hyperbolic (and other non-exponential) Q-values and a new multi-horizon auxiliary task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantifying Exposure Bias for Neural Language Generation

Paper content:
The exposure bias problem refers to the training-inference discrepancy caused by teacher forcing in maximum likelihood estimation (MLE) training for auto-regressive neural network language models (LM).
It has been regarded as a central problem for natural language generation (NLG) model training.
Although a lot of algorithms have been proposed to avoid teacher forcing and therefore to alleviate exposure bias, there is little work showing how serious the exposure bias problem is.
In this work, we first identify the auto-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias.
We then develop a precise, quantifiable definition for exposure bias.
However, according to our measurements in controlled experiments, there's only around 3% performance gain when the training-inference discrepancy is completely removed.
Our results suggest the exposure bias problem could be much less serious than it is currently assumed to be.

assistant: text: We show that exposure bias could be much less serious than it is currently assumed to be for MLE LM training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning

Paper content:
Meta-learning methods, most notably Model-Agnostic Meta-Learning (Finn et al, 2017) or MAML, have achieved great success in adapting to new tasks quickly, after having been trained on similar tasks.
The mechanism behind their success, however, is poorly understood.
We begin this work with an experimental analysis of MAML, finding that deep models are crucial for its success, even given sets of simple tasks where a linear model would suffice on any individual task.
Furthermore, on image-recognition tasks, we find that the early layers of MAML-trained models learn task-invariant features, while later layers are used for adaptation, providing further evidence that these models require greater capacity than is strictly necessary for their individual tasks.
Following our findings, we propose a method which enables better use of model capacity at inference time by separating the adaptation aspect of meta-learning into parameters that are only used for adaptation but are not part of the forward model.
We find that our approach enables more effective meta-learning in smaller models, which are suitably sized for the individual tasks.

assistant: text: We find that deep models are crucial for MAML to work and propose a method which enables effective meta-learning in smaller models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochasticity and skip connections improve knowledge transfer

Paper content:
Deep neural networks have achieved state-of-the-art performance in various fields, but they have to be scaled down to be used for real-world applications.
As a means to reduce the size of a neural network while preserving its performance, knowledge transfer has brought a lot of attention.
One popular method of knowledge transfer is knowledge distillation (KD), where softened outputs of a pre-trained teacher network help train student networks.
Since KD, other transfer methods have been proposed, and they mainly focus on loss functions, activations of hidden layers, or additional modules to transfer knowledge well from teacher networks to student networks.
In this work, we focus on the structure of a teacher network to get the effect of multiple teacher networks without additional resources.
We propose changing the structure of a teacher network to have stochastic blocks and skip connections.
In doing so, a teacher network becomes the aggregate of a huge number of paths.
In the training phase, each sub-network is generated by dropping stochastic blocks randomly and used as a teacher network.
This allows training the student network with multiple teacher networks and further enhances the student network on the same resources in a single teacher network.
We verify that the proposed structure brings further improvement to student networks on benchmark datasets.

assistant: text: The goal of this paper is to get the effect of multiple teacher networks by exploiting stochastic blocks and skip connections.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Defending Against Adversarial Examples by Regularized Deep Embedding

Paper content:
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Inspired by the observation that the intrinsic dimension of image data is much smaller than its pixel space dimension and the vulnerability of neural networks grows with the input dimension, we propose to embed high-dimensional input images into a low-dimensional space to perform classification.
However, arbitrarily projecting the input images to a low-dimensional space without regularization will not improve the robustness of deep neural networks.
We propose a new framework, Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization.
Experimental results on several benchmark datasets show that, our proposed framework achieves state-of-the-art performance against strong adversarial attack methods.

assistant: text: A general and easy-to-use framework that improves the adversarial robustness of deep classification models through embedding regularization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage

Paper content:
Much of the focus in the design of deep neural networks had been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios.
As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance.
In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage by introducing NetScore, a new metric designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network.
In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset.
The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field.

assistant: text: We introduce NetScore, new metric designed to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Measuring and regularizing networks in function space

Paper content:
To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs.
Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but that the correspondence has not been tested.
Here, we show that it is simple and computationally feasible to calculate distances between functions in a $L^2$ Hilbert space.
We examine how typical networks behave in this space, and compare how parameter $\ell^2$ distances compare to function $L^2$ distances between various points of an optimization trajectory.
We find that the two distances are nontrivially related.
In particular, the $L^2/\ell^2$ ratio decreases throughout optimization, reaching a steady value around when test error plateaus.
We then investigate how the $L^2$ distance could be applied directly to optimization.
We first propose that in multitask learning, one can avoid catastrophic forgetting by directly limiting how much the input/output function changes between tasks.
Secondly, we propose a new learning rule that constrains the distance a network can travel through $L^2$-space in any one update.
This allows new examples to be learned in a way that minimally interferes with what has previously been learned.
These applications demonstrate how one can measure and regularize function distances directly, without relying on parameters or local approximations like loss curvature.

assistant: text: We find movement in function space is not proportional to movement in parameter space during optimization. We propose a new natural-gradient style optimizer to address this.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Siamese Capsule Networks 

Paper content:
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB.
Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable.
Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points.
In doing so we introduce \textit{Siamese Capsule Networks}, a new variant that can be used for pairwise learning tasks.
We find that the model improves over baselines in the few-shot learning setting, suggesting that capsule networks are efficient at learning discriminative representations when given few samples.
We find that \textit{Siamese Capsule Networks} perform well against strong baselines on both pairwise learning datasets when trained using a contrastive loss with $\ell_2$-normalized capsule encoded pose features, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.

assistant: text: A pairwise learned capsule network that performs well on face verification tasks given limited labeled data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: UNSUPERVISED MONOCULAR DEPTH ESTIMATION WITH CLEAR BOUNDARIES

Paper content:
Unsupervised monocular depth estimation has made great progress after deep
learning is involved.
Training with binocular stereo images is considered as a
good option as the data can be easily obtained.
However, the depth or disparity
prediction results show poor performance for the object boundaries.
The main
reason is related to the handling of occlusion areas during the training.
In this paper,
we propose a novel method to overcome this issue.
Exploiting disparity maps
property, we generate an occlusion mask to block the back-propagation of the occlusion
areas during image warping.
We also design new networks with flipped
stereo images to induce the networks to learn occluded boundaries.
It shows that
our method achieves clearer boundaries and better evaluation results on KITTI
driving dataset and Virtual KITTI dataset.

assistant: text: This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

Paper content:
We focus on temporal self-supervision for GAN-based video generation tasks.
While adversarial training successfully yields generative models for a variety of areas, temporal relationship in the generated data is much less explored.
This is crucial for sequential generation tasks, e.g. video super-resolution and unpaired video translation.
For the former, state-of-the-art methods often favor simpler norm losses such as L2 over adversarial training.
However, their averaging nature easily leads to temporally smooth results with an undesirable lack of spatial detail.
For unpaired video translation, existing approaches modify the generator networks to form spatio-temporal cycle consistencies.
In contrast, we focus on improving the learning objectives and propose a temporally self-supervised algorithm.
For both tasks, we show that temporal adversarial learning is key to achieving temporally coherent solutions without sacrificing spatial detail.
We also propose a novel Ping-Pong loss to improve the long-term temporal consistency.
It effectively prevents recurrent networks from accumulating artifacts temporally without depressing detailed features.
We also propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.
A series of user studies confirms the rankings computed with these metrics.

assistant: text: We propose temporal self-supervisions for learning stable temporal functions with GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Cascade Adversarial Machine Learning Regularized with a Unified Embedding

Paper content:
Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks.
To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy.
Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training.
We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained.
We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation.
During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial).
Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks.
We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.

assistant: text: Cascade adversarial training + low level similarity learning improve robustness against both white box and black box attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks

Paper content:
Solving long-horizon sequential decision making tasks in environments with sparse rewards is a longstanding problem in reinforcement learning (RL) research.
Hierarchical Reinforcement Learning (HRL) has held the promise to enhance the capabilities of RL agents via operation on different levels of temporal abstraction.
Despite the success of recent works in dealing with inherent nonstationarity and sample complexity, it remains difficult to generalize to unseen environments and to transfer different layers of the policy to other agents.
In this paper, we propose a novel HRL architecture, Hierarchical Decompositional Reinforcement Learning (HiDe), which allows decomposition of the hierarchical layers into independent subtasks, yet allows for joint training of all layers in end-to-end manner.
The main insight is to combine a control policy on a lower level with an image-based planning policy on a higher level.
We evaluate our method on various complex continuous control tasks for navigation, demonstrating that generalization across environments and transfer of higher level policies can be achieved.
See videos https://sites.google.com/view/hide-rl

assistant: text: Learning Functionally Decomposed Hierarchies for Continuous Navigation Tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Role-Wise Data Augmentation for Knowledge Distillation

Paper content:
Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the teacher) into another model (the student), where typically, the teacher has a greater capacity (e.g., more parameters or higher bit-widths).
To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated.
Due to the difference in model capacities, the student may not benefit fully from the same data points on which the teacher is trained.
On the other hand, a human teacher may demonstrate a piece of knowledge with individualized examples adapted to a particular student, for instance, in terms of her cultural background and interests.
Inspired by this behavior, we design data augmentation agents with distinct roles to facilitate knowledge distillation.
Our data augmentation agents generate distinct training data for the teacher and student, respectively.
We focus specifically on KD when the teacher network has greater precision (bit-width) than the student network.
We find empirically that specially tailored data points enable the teacher's knowledge to be demonstrated more effectively to the student.
We compare our approach with existing KD methods on training popular neural architectures and demonstrate that role-wise data augmentation improves the effectiveness of KD over strong prior approaches.
The code for reproducing our results will be made publicly available.

assistant: text: We study whether and how adaptive data augmentation and knowledge distillation can be leveraged simultaneously in a synergistic manner for better training student networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semi-Supervised Learning via New Deep Network Inversion

Paper content:
We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems.
The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10.
Through the introduced method, residual networks are for the first time applied to semi-supervised tasks.
Experiments with one-dimensional signals highlight the generality of the method.
Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.

assistant: text: We exploit an inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework applicable to many topologies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Context-aware Forecasting for Multivariate Stationary Time-series

Paper content:
The domain of time-series forecasting has been extensively studied because it is of fundamental importance in many real-life applications.
Weather prediction, traffic flow forecasting or sales are compelling examples of sequential phenomena.
Predictive models generally make use of the relations between past and future values.
However, in the case of stationary time-series, observed values also drastically depend on a number of exogenous features that can be used to improve forecasting quality.
In this work, we propose a change of paradigm which consists in learning such features in embeddings vectors within recurrent neural networks.
We apply our framework to forecast smart cards tap-in logs in the Parisian subway network.
Results show that context-embedded models perform quantitatively better in one-step ahead and multi-step ahead forecasting.

assistant: text: In order to forecast multivariate stationary time-series we learn embeddings containing contextual features within a RNN; we apply the framework on public transportation data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Large Margin Neural Language Models

Paper content:
Neural language models (NLMs) are generative, and they model the distribution of grammatical sentences.
Trained on huge corpus, NLMs are pushing the limit of modeling accuracy.
Besides, they have also been applied to supervised learning tasks that decode text, e.g., automatic speech recognition (ASR).
By re-scoring the n-best list, NLM can select grammatically more correct candidate among the list, and significantly reduce word/char error rate.
However, the generative nature of NLM may not guarantee a discrimination between “good” and “bad” (in a task-specific sense) sentences, resulting in suboptimal performance.
This work proposes an approach to adapt a generative NLM to a discriminative one.
Different from the commonly used maximum likelihood objective, the proposed method aims at enlarging the margin between the “good” and “bad” sentences.
It is trained end-to-end and can be widely applied to tasks that involve the re-scoring of the decoded text.
Significant gains are observed in both ASR and statistical machine translation (SMT) tasks.

assistant: text: Enhance the language model for supervised learning task
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
This paper fosters the idea that deep learning methods can be sided to classical
visual odometry pipelines to improve their accuracy and to produce uncertainty
models to their estimations.
We show that the biases inherent to the visual odom-
etry process can be faithfully learnt and compensated for, and that a learning ar-
chitecture associated to a probabilistic loss function can jointly estimate a full
covariance matrix of the residual errors, defining a heteroscedastic error model.
Experiments on autonomous driving image sequences and micro aerial vehicles
camera acquisitions assess the possibility to concurrently improve visual odome-
try and estimate an error associated to its outputs.

assistant: text: This paper discusses different methods of pairing VO with deep learning and proposes a simultaneous prediction of corrections and uncertainty.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Building Deep Equivariant Capsule Networks

Paper content:
Capsule networks are constrained by the parameter-expensive nature of their layers, and the general lack of provable equivariance guarantees.
We present a variation of capsule networks that aims to remedy this.
We identify that learning all pair-wise part-whole relationships between capsules of successive layers is inefficient.
Further, we also realise that the choice of prediction networks and the routing mechanism are both key to equivariance.
Based on these, we propose an alternative framework for capsule networks that learns to projectively encode the manifold of pose-variations, termed the space-of-variation (SOV), for every capsule-type of each layer.
This is done using a trainable, equivariant function defined over a grid of group-transformations.
Thus, the prediction-phase of routing involves projection into the SOV of a deeper capsule using the corresponding function.
As a specific instantiation of this idea, and also in order to reap the benefits of increased parameter-sharing, we use type-homogeneous group-equivariant convolutions of shallower capsules in this phase.
We also introduce an equivariant routing mechanism based on degree-centrality.
We show that this particular instance of our general model is equivariant, and hence preserves the compositional representation of an input under transformations.
We conduct several experiments on standard object-classification datasets that showcase the increased transformation-robustness, as well as general performance, of our model to several capsule baselines.

assistant: text: A new scalable, group-equivariant model for capsule networks that preserves compositionality under transformations, and is empirically more transformation-robust to older capsule network models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Turing Completeness of Modern Neural Network Architectures

Paper content:
Alternatives to recurrent neural networks, in particular, architectures based on attention or convolutions, have been gaining momentum for processing input sequences.
In spite of their relevance, the computational properties of these alternatives have not yet been fully explored.
We study the computational power of two of the most paradigmatic architectures exemplifying these mechanisms: the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever, 2016).
We show both models to be Turing complete exclusively based on their capacity to compute and access internal dense representations of the data.
In particular, neither the Transformer nor the Neural GPU requires access to an external memory to become Turing complete.
Our study also reveals some minimal sets of elements needed to obtain these completeness results.

assistant: text: We show that the Transformer architecture and the Neural GPU are Turing complete.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-time-horizon Solar Forecasting Using  Recurrent Neural Network

Paper content:
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and resulting in increased cost of operation.
This research paper proposes a unified architecture for multi-time-horizon solar forecasting for short and long-term predictions using Recurrent Neural Networks (RNN).
The paper describes an end-to-end pipeline to implement the architecture along with methods to test and validate the performance of the prediction model.
The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best performing methods which use one model for each time-horizon.
The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.

assistant: text: This paper proposes a Unified Recurrent Neural Network Architecture for  short-term multi-time-horizon solar forecasting and validates the forecast performance gains over the previously reported methods
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Anytime Neural Network: a Versatile Trade-off Between Computation and Accuracy

Paper content:
We present an approach for anytime predictions in deep neural networks (DNNs).
For each test sample, an anytime predictor produces a coarse result quickly, and then continues to refine it until the test-time computational budget is depleted.
Such predictors can address the growing computational problem of DNNs by automatically adjusting to varying test-time budgets.
In this work, we study a \emph{general} augmentation to feed-forward networks to form anytime neural networks (ANNs) via auxiliary predictions and losses.
Specifically, we point out a blind-spot in recent studies in such ANNs: the importance of high final accuracy.
In fact, we show on multiple recognition data-sets and architectures that by having near-optimal final predictions in small anytime models, we can effectively double the speed of large ones to reach corresponding accuracy level.
We achieve such speed-up with simple weighting of anytime losses that oscillate during training.
We also assemble a sequence of exponentially deepening ANNs, to achieve both theoretically and practically near-optimal anytime results at any budget, at the cost of a constant fraction of additional consumed budget.

assistant: text: By focusing more on the final predictions in anytime predictors (such as the very recent Multi-Scale-DenseNets), we make small anytime models to outperform large ones that don't have such focus.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: One Demonstration Imitation Learning

Paper content:
We develop a new algorithm for imitation learning from a single expert demonstration.
In contrast to many previous one-shot imitation learning approaches, our algorithm does not assume access to more than one expert demonstration during the training phase.
Instead, we leverage an exploration policy to acquire unsupervised trajectories, which are then used to train both an encoder and a context-aware imitation policy.
The optimization procedures for the encoder, imitation learner, and exploration policy are all tightly linked.
This linking creates a feedback loop wherein the exploration policy collects new demonstrations that challenge the imitation learner, while the encoder attempts to help the imitation policy to the best of its abilities.
We evaluate our algorithm on 6 MujoCo robotics tasks.

assistant: text: Unsupervised self-imitation algorithm capable of inference from a single expert demonstration.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Learning of Automotive 3D Crash Simulations using LSTMs

Paper content:
Long short-term memory (LSTM) networks allow to exhibit temporal dynamic behavior with feedback connections and seem a natural choice for learning sequences of 3D meshes.
We introduce an approach for dynamic mesh representations as used for numerical simulations of car crashes.
To bypass the complication of using 3D meshes, we transform the surface mesh sequences into spectral descriptors that efficiently encode the shape.
A two branch LSTM based network architecture is chosen to learn the representations and dynamics of the crash during the simulation.
The architecture is based on unsupervised video prediction by an LSTM without any convolutional layer.
It uses an encoder LSTM to map an input sequence into a fixed length vector representation.
On this representation one decoder LSTM performs the reconstruction of the input sequence, while the other decoder LSTM predicts the future behavior by receiving initial steps of the sequence as seed.
The spatio-temporal error behavior of the model is analysed to study how well the model can extrapolate the learned spectral descriptors into the future, that is, how well it has learned to represent the underlying dynamical structural mechanics.
Considering that only a few training examples are available, which is the typical case for numerical simulations, the network performs very well.

assistant: text: A two branch LSTM based network architecture learns the representation and dynamics of 3D meshes of numerical crash simulations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Log-DenseNet: How to Sparsify a DenseNet

Paper content:
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency.
In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones.
However, DenseNet's extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive.
This work first experimentally shows that one key advantage of skip connections is to have short distances among feature layers during backpropagation.
Specifically, using a fixed number of skip connections, the connection patterns with shorter backpropagation distance among layers have more accurate predictions.
Following this insight, we propose a connection template, Log-DenseNet, which, in comparison to DenseNet,  only slightly increases the backpropagation distances among layers from 1 to  ($1 + \log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$.
Hence, \logdenses are easier to scale than DenseNets, and no longer require careful GPU memory management.
We demonstrate the effectiveness of our design principle by showing better performance than DenseNets on tabula rasa semantic segmentation, and competitive results on visual recognition.

assistant: text: We show shortcut connections should be placed in patterns that minimize between-layer distances during backpropagation, and design networks that achieve log L distances using L log(L) connections.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Value Propagation Networks

Paper content:
We present Value Propagation (VProp), a parameter-efficient differentiable planning module built on Value Iteration which can successfully be trained in a reinforcement learning fashion to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments.
We evaluate on configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes.
Furthermore, we show that the module enables to learn to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems.

assistant: text: We propose Value Propagation, a novel end-to-end planner which can learn to solve 2D navigation tasks via Reinforcement Learning, and that generalizes to larger and dynamic environments.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: HTN Planning with Semantic Attachments

Paper content:
Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task.
While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls.
Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time.
In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner.
The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables).
We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.

assistant: text: An approach to perform HTN planning using external procedures to evaluate predicates at runtime (semantic attachments).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual EBMs: Does Real vs. Fake Text Discrimination Generalize?

Paper content:
Energy-based models (EBMs), a.k.a.
un-normalized models, have had recent successes in continuous spaces.
However, they have not been successfully applied to model text sequences.
While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficult.
In part, this is because standard gradient-based methods are not readily applicable when the input is high-dimensional and discrete.
Here, we side-step this issue by generating negatives using pre-trained auto-regressive language models.
The EBM then works
in the {\em residual} of the language model; and is trained to discriminate real text from text generated by the auto-regressive models.
We  investigate the generalization ability of residual EBMs, a pre-requisite for using them in other applications.
We extensively analyze generalization for the task of classifying whether an input is machine or human generated, a natural task given the training loss and how we mine negatives.
Overall, we observe that EBMs can generalize remarkably well to changes in the architecture of the generators producing negatives.
However, EBMs exhibit more sensitivity to the training set used by such generators.

assistant: text: A residual EBM for text whose formulation is equivalent to discriminating between human and machine generated text. We study its generalization behavior.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Do LSTMs Learn Compositionally?

Paper content:
LSTM-based language models exhibit compositionality in their representations, but how this behavior emerges over the course of training has not been explored.
Analyzing synthetic data experiments with contextual decomposition, we find that LSTMs learn long-range dependencies compositionally by building them from shorter constituents during training.
Consider the process of backpropagation through time for a language model.
As an example, the language model should learn that an occurrence of "either" increases the later likelihood of "or".
To do so, it must backpropagate information from the occurrence of "or" through some intervening constituent, which we will refer to as a conduit because the association of either/or is carried through it to affect the representation of "either".
Perhaps it encounters a training example that uses a conduit that is predictable by being structured in familiar ways, here italicized: "Either Socrates is mortal or not all men are mortal."
However, what if the conduit is unpredictable and the structure cannot be interpreted by the model, for example, if the conduit includes unknown tokens, as in: "Either slithy toves gyre or mome raths outgrabe"?
Which conduit will carry the gradient from "or" to "either" easily?Formally
, as the gradient of the error e t at timestep t is backpropagated k timesteps through the hidden state h: DISPLAYFORM0 The backpropagated message is multiplied repeatedly by the gradients associated with each item in the conduit. If the
recurrence derivatives ∂h i+1 ∂h i are large at some parameter, the correspondingly larger backpropagated gradient ∂et ∂h t−k will accelerate descent in that direction.When we ask which conduit will carry the gradient message to learn a long-range dependency faster, the answer will depend on the magnitude and distribution of the recurrence gradients. If the
language model relies on linguistic structure in the conduit in order to pass the message effectively, then the more predictable conduit will facilitate learning a long-range pattern.In order to investigate whether long-range dependencies are built from short constituents, we train models on synthetic data which varies the predictability of short sequences. We find
that memorizing local patterns allows a language model to learn a long-range dependency faster but ultimately inhibits its ability to fully acquire longrange rules.
We confirm that the longer the span of a rule, the more examples are required for an LSTM model to effectively learn the rule.
We then find1 that a more predictable conduit between the rule symbols promotes the early learning of the rule, implying that the process by which an LSTM learns long-range rules is compositional.
However, the representation learned through the predictable conduit ultimately prevents the model from confidently learning these long-range connections.

assistant: text: LSTMs learn long-range dependencies compositionally by building them from shorter constituents over the course of training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Attentional Credit Assignment for Transfer in Reinforcement Learning

Paper content:
The ability to transfer knowledge to novel environments and tasks is a sensible desiderata for general learning agents.
Despite the apparent promises, transfer in RL is still an open and little exploited research area.
In this paper, we take a brand-new perspective about transfer: we suggest that the ability to assign credit unveils structural invariants in the tasks that can be transferred to make RL more sample efficient.
Our main contribution is Secret, a novel approach to transfer learning for RL that uses a backward-view credit assignment mechanism based on a self-attentive architecture.
Two aspects are key to its generality: it learns to assign credit as a separate offline supervised process and exclusively modifies the reward function.
Consequently, it can be supplemented by transfer methods that do not modify the reward function and it can be plugged on top of any RL algorithm.

assistant: text: Secret is a transfer method for RL based on the transfer of credit assignment.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Policies: Attacking Deep Reinforcement Learning

Paper content:
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
However, an attacker is not usually able to directly modify another agent's observations.
This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial?
We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents.
The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior.
We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent.
Videos are available at https://attackingrl.github.io.

assistant: text: Deep RL policies can be attacked by other agents taking actions so as to create natural observations that are adversarial.