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

Title: Deep Frank-Wolfe For Neural Network Optimization

Paper content:
Learning a deep neural network requires solving a challenging optimization problem: it is a high-dimensional, non-convex and non-smooth minimization problem with a large number of terms.
The current practice in neural network optimization is to rely on the stochastic gradient descent (SGD) algorithm or its adaptive variants.
However, SGD requires a hand-designed schedule for the learning rate.
In addition, its adaptive variants tend to produce solutions that generalize less well on unseen data than SGD with a hand-designed schedule.
We present an optimization method that offers empirically the best of both worlds: our algorithm yields good generalization performance while requiring only one hyper-parameter.
Our approach is based on a composite proximal framework, which exploits the compositional nature of deep neural networks and can leverage powerful convex optimization algorithms by design.
Specifically, we employ the Frank-Wolfe (FW) algorithm for SVM, which computes an optimal step-size in closed-form at each time-step.
We further show that the descent direction is given by a simple backward pass in the network, yielding the same computational cost per iteration as SGD.
We present experiments on the CIFAR and SNLI data sets, where we demonstrate the significant superiority of our method over Adam, Adagrad, as well as the recently proposed BPGrad and AMSGrad.
Furthermore, we compare our algorithm to SGD with a hand-designed learning rate schedule, and show that it provides similar generalization while often converging faster.
The code is publicly available at https://github.com/oval-group/dfw.

assistant: text: We train neural networks by locally linearizing them and using a linear SVM solver (Frank-Wolfe) at each iteration.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
We propose a method for joint image and per-pixel annotation synthesis with GAN.
We demonstrate that GAN has good high-level representation of target data that can be easily projected to semantic segmentation masks.
This method can be used to create a training dataset for teaching separate semantic segmentation network.
Our experiments show that such segmentation network successfully generalizes on real data.
Additionally, the method outperforms supervised training when the number of training samples is small, and works on variety of different scenes and classes.
The source code of the proposed method will be publicly available.

assistant: text: GAN-based method for joint image and per-pixel annotation synthesis
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Checking Functional Modularity in DNN By Biclustering Task-specific Hidden Neurons

Paper content:
While real brain networks exhibit functional modularity, we investigate whether functional mod- ularity also exists in Deep Neural Networks (DNN) trained through back-propagation.
Under the hypothesis that DNN are also organized in task-specific modules, in this paper we seek to dissect a hidden layer into disjoint groups of task-specific hidden neurons with the help of relatively well- studied neuron attribution methods.
By saying task-specific, we mean the hidden neurons in the same group are functionally related for predicting a set of similar data samples, i.e. samples with similar feature patterns.
We argue that such groups of neurons which we call Functional Modules can serve as the basic functional unit in DNN.
We propose a preliminary method to identify Functional Modules via bi- clustering attribution scores of hidden neurons.
We find that first, unsurprisingly, the functional neurons are highly sparse, i.e., only a small sub- set of neurons are important for predicting a small subset of data samples and, while we do not use any label supervision, samples corresponding to the same group (bicluster) show surprisingly coherent feature patterns.
We also show that these Functional Modules perform a critical role in discriminating data samples through ablation experiment.

assistant: text: We develop an approach to parcellate a hidden layer in DNN into functionally related groups, by applying spectral coclustering on the attribution scores of hidden neurons.
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.

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

Paper content:
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks.
However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning.
Both of these challenges severely limit the applicability of such methods to complex, real-world domains.
In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework.
In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible.
Prior deep RL methods based on this framework have been formulated as either off-policy Q-learning, or on-policy policy gradient methods.
By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods.
Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.
We presented soft actor-critic (SAC), an off-policy maximum entropy deep reinforcement learning algorithm that provides sample-efficient learning while retaining the benefits of entropy maximization and stability.
Our theoretical results derive soft policy iteration, which we show to converge to the optimal policy.
From this result, we can formulate a soft actor-critic algorithm, and we empirically show that it outperforms state-of-the-art model-free deep RL methods, including the off-policy DDPG algorithm and the on-policy TRPO algorithm.
In fact, the sample efficiency of this approach actually exceeds that of DDPG by a substantial margin.
Our results suggest that stochastic, entropy maximizing reinforcement learning algorithms can provide a promising avenue for improved robustness and stability, and further exploration of maximum entropy methods, including methods that incorporate second order information (e.g., trust regions BID21 ) or more expressive policy classes is an exciting avenue for future work.

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

Title: Collecting Entailment Data for Pretraining: New Protocols and Negative Results

Paper content:
Textual entailment (or NLI) data has proven useful as pretraining data for tasks requiring language understanding, even when building on an already-pretrained model like RoBERTa.
The standard protocol for collecting NLI was not designed for the creation of pretraining data, and it is likely far from ideal for this purpose.
With this application in mind we propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples.
Using these alternatives and a simple MNLIbased baseline, we collect and compare five new 9k-example training sets.
Our primary results are largely negative, with none of these new methods showing major improvements in transfer learning.
However, we make several observations that should inform future work on NLI data, such as that the use of automatically provided seed sentences for inspiration improves the quality of the resulting data on most measures, and all of the interventions we investigated dramatically reduce previously observed issues with annotation artifacts.

assistant: text: We propose four new ways of collecting NLI data. Some help slightly as pretraining data, all help reduce annotation artifacts.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Paper content:
Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability.
However, visualization and understanding of GANs is largely missing.
How does a GAN represent our visual world internally?
What causes the artifacts in GAN results?
How do architectural choices affect GAN learning?
Answering such questions could enable us to develop new insights and better models.
In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level.
We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method.
Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.
Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images.
We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene.
We provide open source interpretation tools to help peer researchers and practitioners better understand their GAN models.

assistant: text: GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Federated User Representation Learning

Paper content:
Collaborative personalization, such as through learned user representations (embeddings), can improve the prediction accuracy of neural-network-based models significantly.
We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and resource-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting.
FURL divides model parameters into federated and private parameters.
Private parameters, such as private user embeddings, are trained locally, but unlike federated parameters, they are not transferred to or averaged on the server.
We show theoretically that this parameter split does not affect training for most model personalization approaches.
Storing user embeddings locally not only preserves user privacy, but also improves memory locality of personalization compared to on-server training.
We evaluate FURL on two datasets, demonstrating a significant improvement in model quality with 8% and 51% performance increases, and approximately the same level of performance as centralized training with only 0% and 4% reductions.
Furthermore, we show that user embeddings learned in FL and the centralized setting have a very similar structure, indicating that FURL can learn collaboratively through the shared parameters while preserving user privacy.

assistant: text: We propose Federated User Representation Learning (FURL), a simple, scalable, privacy-preserving and bandwidth-efficient way to utilize existing neural personalization techniques in the Federated Learning (FL) setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semi-supervised Autoencoding Projective Dependency Parsing

Paper content:
We describe two end-to-end autoencoding models for semi-supervised graph-based dependency parsing.
The first model is a Local Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Global Autoencoding Parser (GAP) encoding the input into dependency trees as latent variables, with exact inference.
Both models consist of two parts: an encoder enhanced by deep neural networks (DNN) that can utilize the contextual information to encode the input into latent variables, and a decoder which is a generative model able to reconstruct the input.
Both LAP and GAP admit a unified structure with different loss functions for labeled and unlabeled data with shared parameters.
We conducted experiments on WSJ and UD dependency parsing data sets, showing that our models can exploit the unlabeled data to boost the performance given a limited amount of labeled data.

assistant: text: We describe two end-to-end autoencoding parsers for semi-supervised graph-based dependency parsing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: VideoEpitoma: Efficient Recognition of Long-range Actions

Paper content:
CNNs are widely successful in recognizing human actions in videos, albeit with a great cost of computation.
This cost is significantly higher in the case of long-range actions, where a video can span up to a few minutes, on average.
The goal of this paper is to reduce the computational cost of these CNNs, without sacrificing their performance.
We propose VideoEpitoma,  a neural network architecture comprising two modules: a timestamp selector and a video classifier.
Given a long-range video of thousands of timesteps, the selector learns to choose only a few but most representative timesteps for the video.
This selector resides on top of a lightweight CNN such as MobileNet and uses a novel gating module to take a binary decision: consider or discard a video timestep.
This decision is conditioned on both the timestep-level feature and the video-level consensus.
A heavyweight CNN model such as I3D takes the selected frames as input and performs video classification.
Using off-the-shelf video classifiers, VideoEpitoma reduces the computation by up to 50\% without compromising the accuracy.
In addition, we show that if trained end-to-end, the selector learns to make better choices to the benefit of the classifier, despite the selector and the classifier residing on two different CNNs.
Finally, we report state-of-the-art results on two datasets for long-range action recognition: Charades and Breakfast Actions, with much-reduced computation.
In particular, we match the accuracy of I3D by using less than half of the computation.

assistant: text: Efficient video classification using frame-based conditional gating module for selecting most-dominant frames, followed by temporal modeling and classifier.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Training of Neural Encoding Models on Population Spike Trains

Paper content:
Neural population responses to sensory stimuli can exhibit both nonlinear stimulus- dependence and richly structured shared variability.
Here, we show how adversarial training can be used to optimize neural encoding models to capture both the deterministic and stochastic components of neural population data.
To account for the discrete nature of neural spike trains, we use the REBAR method to estimate unbiased gradients for adversarial optimization of neural encoding models.
We illustrate our approach on population recordings from primary visual cortex.
We show that adding latent noise-sources to a convolutional neural network yields a model which captures both the stimulus-dependence and noise correlations of the population activity.

assistant: text: We show how neural encoding models can be trained to capture both the signal and spiking variability of neural population data using GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Geometric Operator Convolutional Neural Network

Paper content:
The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems.
We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain knowledge, wherein the kernel of the first convolutional layer is replaced with a kernel generated by a geometric operator function.
This framework integrates many conventional geometric operators, which allows it to adapt to a diverse range of problems.
Under certain conditions, we theoretically analyze the convergence and the bound of the generalization errors between GO-CNNs and common CNNs.
Although the geometric operator convolution kernels have fewer trainable parameters than common convolution kernels, the experimental results indicate that GO-CNN performs more accurately than common CNN on CIFAR-10/100.
Furthermore, GO-CNN reduces dependence on the amount of training examples and enhances adversarial stability.

assistant: text: Traditional image processing algorithms are combined with Convolutional Neural Networks，a new neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Set Autoencoder: Unsupervised Representation Learning for Sets

Paper content:
We propose the set autoencoder, a model for unsupervised representation learning for sets of elements.
It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.
In contrast to sequences, sets are permutation invariant.
The proposed set autoencoder considers this fact, both with respect to the input as well as the output of the model.
On the input side, we adapt a recently-introduced recurrent neural architecture using a content-based attention mechanism.
On the output side, we use a stable marriage algorithm to align predictions to labels in the learning phase.
We train the model on synthetic data sets of point clouds and show that the learned representations change smoothly with translations in the inputs, preserve distances in the inputs, and that the set size is represented directly.
We apply the model to supervised tasks on the point clouds using the fixed-size latent representation.
For a number of difficult classification problems, the results are better than those of a model that does not consider the permutation invariance.
Especially for small training sets, the set-aware model benefits from unsupervised pretraining.

assistant: text: We propose the set autoencoder, a model for unsupervised representation learning for sets of elements.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Graph Infomax

Paper content:
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.
The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks.
In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups.
We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

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

Title: Deep Mean Field Theory: Layerwise Variance and Width Variation as Methods to Control Gradient Explosion

Paper content:
A recent line of work has studied the statistical properties of neural networks to great success from a {\it mean field theory} perspective, making and verifying very precise predictions of neural network behavior and test time performance.
In this paper, we build upon these works to explore two methods for taming the behaviors of random residual networks (with only fully connected layers and no batchnorm).
The first method is {\it width variation (WV)}, i.e. varying the widths of layers as a function of depth.
We show that width decay reduces gradient explosion without affecting the mean forward dynamics of the random network.
The second method is {\it variance variation (VV)}, i.e. changing the initialization variances of weights and biases over depth.
We show VV, used appropriately, can reduce gradient explosion of tanh and ReLU resnets from $\exp(\Theta(\sqrt L))$ and $\exp(\Theta(L))$ respectively to constant $\Theta(1)$.
A complete phase-diagram is derived for how variance decay affects different dynamics, such as those of gradient and activation norms.
In particular, we show the existence of many phase transitions where these dynamics switch between exponential, polynomial, logarithmic, and even constant behaviors.
Using the obtained mean field theory, we are able to track surprisingly well how VV at initialization time affects training and test time performance on MNIST after a set number of epochs: the level sets of test/train set accuracies coincide with the level sets of the expectations of certain gradient norms or of metric expressivity (as defined in \cite{yang_meanfield_2017}), a measure of expansion in a random neural network.
Based on insights from past works in deep mean field theory and information geometry, we also provide a new perspective on the gradient explosion/vanishing problems: they lead to ill-conditioning of the Fisher information matrix, causing optimization troubles.

assistant: text: By setting the width or the initialization variance of each layer differently, we can actually subdue gradient explosion problems in residual networks (with fully connected layers and no batchnorm). A mathematical theory is developed that not only tells you how to do it, but also surprisingly is able to predict, after you apply such tricks, how fast your network trains to achieve a certain test set performance. This is some black magic stuff, and it's called "Deep Mean Field Theory."
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies

Paper content:
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult.
To date, the vast majority of successful RNN architectures alleviate this problem using nearly-additive connections between states, as introduced by long short-term memory (LSTM).
We take an orthogonal approach and introduce MIST RNNs, a NARX RNN architecture that allows direct connections from the very distant past.
We show that MIST RNNs
1) exhibit superior vanishing-gradient properties in comparison to LSTM and previously-proposed NARX RNNs;
2) are far more efficient than previously-proposed NARX RNN architectures, requiring even fewer computations than LSTM; and
3) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies.

assistant: text: We introduce MIST RNNs, which a) exhibit superior vanishing-gradient properties in comparison to LSTM; b) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies; and c) are much more efficient than previously-proposed NARX RNNs, with even fewer parameters and operations than LSTM.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Representations in Reinforcement Learning: an Information Bottleneck Approach

Paper content:
The information bottleneck principle is an elegant and useful approach to representation learning.
In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms.We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound.
Then, we maximize this lower bound with the Stein variational (SV) gradient method.
We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO).
Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly.
Finally, we study the information-bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE).
We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process.
We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.

assistant: text: Derive an information bottleneck framework in reinforcement learning and some simple relevant theories and tools.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams

Paper content:
In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget.
In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time.
The suggested method acquires features incrementally based on a context-aware feature-value function.
We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature.
Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function.
Furthermore, we suggest sharing representations between the class predictor and value function estimator networks.
The suggested approach is completely online and is readily applicable to stream learning setups.
The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification.
According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.

assistant: text: An online algorithm for cost-aware feature acquisition and prediction
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: OvA-INN: Continual Learning with Invertible Neural Networks

Paper content:
In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks.
Several solutions have been proposed to tackle this problem but they usually  assume that the user knows which of the tasks to perform at test time on a particular sample, or rely on small samples from previous data and most of them suffer of a substantial drop in accuracy when updated with batches of only one class at a time.
In this article, we propose a new method, OvA-INN, which is able to learn one class at a time and without storing any of the previous data.
To achieve this, for each class, we train a specific Invertible Neural Network to output the zero vector for its class.
At test time, we can predict the class of a sample by identifying which network outputs the vector with the smallest norm.
With this method, we show that we can take advantage of pretrained models by stacking an invertible network on top of a features extractor.
This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets.
In our experiments, we are reaching 72% accuracy on CIFAR-100 after training our model one class at a time.

assistant: text: We propose to train an Invertible Neural Network for each class to perform class-by-class Continual Learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DOM-Q-NET:  Grounded RL on Structured Language

Paper content:
Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning.
However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the states.
In this work, we introduce DOM-Q-NET, a novel architecture for RL-based web navigation to address both of these problems.
It parametrizes Q functions with separate networks for different action categories: clicking a DOM element and typing a string input.
Our model utilizes a graph neural network to represent the tree-structured HTML of a standard web page.
We demonstrate the capabilities of our model on the MiniWoB environment where we can match or outperform existing work without the use of expert demonstrations.
Furthermore, we show 2x improvements in sample efficiency when training in the multi-task setting, allowing our model to transfer learned behaviours across tasks.

assistant: text: Graph-based Deep Q Network for Web Navigation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NAS evaluation is frustratingly hard

Paper content:
Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012.
Despite many great works leading to substantial improvements on a variety of tasks, comparison between different methods is still very much an open issue.
While most algorithms are tested on the same datasets, there is no shared experimental protocol followed by all.
As such, and due to the under-use of ablation studies, there is a lack of clarity regarding why certain methods are more effective than others.
Our first contribution is a benchmark of 8 NAS methods on 5 datasets.
To overcome the hurdle of comparing methods with different search spaces, we propose using a method’s relative improvement over the randomly sampled average architecture, which effectively removes advantages arising from expertly engineered search spaces or training protocols.
Surprisingly, we find that many NAS techniques struggle to significantly beat the average architecture baseline.
We perform further experiments with the commonly used DARTS search space in order to understand the contribution of each component in the NAS pipeline.
These experiments highlight that:
(i) the use of tricks in the evaluation protocol has a predominant impact on the reported performance of architectures;
(ii) the cell-based search space has a very narrow accuracy range, such that the seed has a considerable impact on architecture rankings;
(iii) the hand-designed macrostructure (cells) is more important than the searched micro-structure (operations); and
(iv) the depth-gap is a real phenomenon, evidenced by the change in rankings between 8 and 20 cell architectures.
To conclude, we suggest best practices, that we hope will prove useful for the community and help mitigate current NAS pitfalls, e.g. difficulties in reproducibility and comparison of search methods.
The
code used is available at https://github.com/antoyang/NAS-Benchmark.

assistant: text: A study of how different components in the NAS pipeline contribute to the final accuracy. Also, a benchmark of 8 methods on 5 datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DPSNet: End-to-end Deep Plane Sweep Stereo

Paper content:
Multiview stereo aims to reconstruct scene depth from images acquired by a camera under arbitrary motion.
Recent methods address this problem through deep learning, which can utilize semantic cues to deal with challenges such as textureless and reflective regions.
In this paper, we present a convolutional neural network called DPSNet (Deep Plane Sweep Network) whose design is inspired by best practices of traditional geometry-based approaches.
Rather than directly estimating depth and/or optical flow correspondence from image pairs as done in many previous deep learning methods, DPSNet takes a plane sweep approach that involves building a cost volume from deep features using the plane sweep algorithm, regularizing the cost volume via a context-aware cost aggregation, and regressing the depth map from the cost volume.
The cost volume is constructed using a differentiable warping process that allows for end-to-end training of the network.
Through the effective incorporation of conventional multiview stereo concepts within a deep learning framework, DPSNet achieves state-of-the-art reconstruction results on a variety of challenging datasets.

assistant: text: A convolution neural network for multi-view stereo matching whose design is inspired by best practices of traditional geometry-based approaches
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Posterior Control of Blackbox Generation 

Paper content:
Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints.
This level of fine-grained control can be difficult to obtain in large-scale neural network models.
In this work, we propose a structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm.
Under this formulation, we can include a range of rich, posterior constraints to enforce task-specific knowledge that is effectively trained into the neural model.
This approach allows us to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models.
Experiments consider applications of this approach for text generation and part-of-speech induction.
For natural language generation, we find that this method improves over standard benchmarks, while also providing fine-grained control.

assistant: text: A structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: COLD FUSION: TRAINING SEQ2SEQ MODELS TOGETHER WITH LANGUAGE MODELS

Paper content:
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.
Performance has further been improved by leveraging unlabeled data, often in the form of a language model.
In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task.
We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying
i) faster convergence and better generalization, and
ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.

assistant: text: We introduce a novel method to train Seq2Seq models with language models that converge faster, generalize better and can almost completely transfer to a new domain using less than 10% of labeled data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Advantages and Challenges of Using AI Planning in Cloud Migration

Paper content:
Cloud Migration transforms customer’s data, application and services from original IT platform to one or more cloud en- vironment, with the goal of improving the performance of the IT system while reducing the IT management cost.
The enterprise level Cloud Migration projects are generally com- plex, involves dynamically planning and replanning various types of transformations for up to 10k endpoints.
Currently the planning and replanning in Cloud Migration are generally done manually or semi-manually with heavy dependency on the migration expert’s domain knowledge, which takes days to even weeks for each round of planning or replanning.
As a result, automated planning engine that is capable of gener- ating high quality migration plan in a short time is particu- larly desirable for the migration industry.
In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the challenges the requires attention from the planning and scheduling society.
Automated planning and AI planning have been investigated extensively by researchers and successfully applied in many areas for decades, for example, health care BID0 , semiconductor manufacturing BID1 , and aviation BID2 , to name a few.
Meanwhile, attracted by the promise of the scalability, flexibility and potentially lower cost of the resources, more and more enterprises are considering moving their IT infrastructure and applications to Cloud or Hybrid Cloud service platforms, which is called Cloud Migration in general (Armbrust et. al. 2010, Khajeh-Hosseini, Greenwood, and Sommerville 2010) .
Noticing that the discussions of using AI planning in the Cloud Migration are limited both in academia and in industry, in this short paper we identify the advantages and challenges of applying AI planning to Cloud Migration by
(i) introducing Cloud Migration and its planning problem;
(ii) demonstrate problem feasibility by showing a prototype AI planning model; and
(iii) discuss the limits of current model and future research.

assistant: text: In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the chal- lenges the requires attention from the planning and schedul- ing society.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Recall Traces: Backtracking Models for Efficient Reinforcement Learning

Paper content:
In many environments only a tiny subset of all states yield high reward.
In these cases, few of the interactions with the environment provide a relevant learning signal.
Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them.
To this end, we advocate for the use of a \textit{backtracking model} that predicts the preceding states that terminate at a given high-reward state.
We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and samples which (state, action)-tuples may have led to that high value state.
These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy.
We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards.
Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.

assistant: text: A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding the Relation Between Maximum-Entropy Inverse Reinforcement Learning and Behaviour Cloning

Paper content:
In many settings, it is desirable to learn decision-making and control policies through learning or from expert demonstrations.
The most common approaches under this framework are Behaviour Cloning (BC), and Inverse Reinforcement Learning (IRL).
Recent methods for IRL have demonstrated the capacity to learn effective policies with access to a very limited set of demonstrations, a scenario in which BC methods often fail.
Unfortunately, directly comparing the algorithms for these methods does not provide adequate intuition for understanding this difference in performance.
This is the motivating factor for our work.
We begin by presenting $f$-MAX, a generalization of AIRL (Fu et al., 2018), a state-of-the-art IRL method.
$f$-MAX provides grounds for more directly comparing the objectives for LfD.
We demonstrate that $f$-MAX, and by inheritance AIRL, is a subset of the cost-regularized IRL framework laid out by Ho & Ermon (2016).
We conclude by empirically evaluating the factors of difference between various LfD objectives in the continuous control domain.

assistant: text: Distribution matching through divergence minimization provides a common ground for comparing adversarial Maximum-Entropy Inverse Reinforcement Learning methods to Behaviour Cloning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Multiple Objects at Spatially Distinct Locations

Paper content:
Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions.
Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions.
However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve.
This is especially true for images that should contain multiple distinct objects at different spatial locations.
We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator.
Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed.
The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes.
The global pathway focuses on the image background and the general image layout.
We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set.
Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.
We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.

assistant: text: Extend GAN architecture to obtain control over locations and identities of multiple objects within generated images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Biologically-Plausible Learning Algorithms Can Scale to Large Datasets

Paper content:
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain.
One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways.
To address this “weight transport problem” (Grossberg, 1987), two biologically-plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets.
However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet.
Here, we additionally evaluate the sign-symmetry (SS) algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs.
We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO).
Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks.
These results complement the study by Bartunov et al. (2018) and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.

assistant: text: Biologically plausible learning algorithms, particularly sign-symmetry, work well on ImageNet