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

Title: Efficacy of Pixel-Level OOD Detection for Semantic Segmentation

Paper content:
The detection of out of distribution samples for image classification has been widely researched.
Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing the image to be out of distribution.
This paper adapts state-of-the-art methods for detecting out of distribution images for image classification to the new task of detecting out of distribution pixels, which can localise the unusual objects.
It further experimentally compares the adapted methods on two new datasets derived from existing semantic segmentation datasets using PSPNet and DeeplabV3+ architectures, as well as proposing a new metric for the task.
The evaluation shows that the performance ranking of the compared methods does not transfer to the new task and every method performs significantly worse than their image-level counterparts.

assistant: text: Evaluating pixel-level out-of-distribution detection methods on two new real world datasets using PSPNet and DeeplabV3+.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatically Trading off Time and Variance when Selecting Gradient Estimators

Paper content:
Stochastic gradient descent (SGD) is the workhorse of modern machine learning.
Sometimes, there are many different potential gradient estimators that can be used.
When so, choosing the one with the best tradeoff between cost and variance is important.
This paper analyzes the convergence rates of SGD as a function of time, rather than iterations.
This results in a simple rule to select the estimator that leads to the best optimization convergence guarantee.
This choice is the same for different variants of SGD, and with different assumptions about the objective (e.g. convexity or smoothness).
Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given.
Then, we extend to infinite pools of estimators, where each one is indexed by control variate weights.
This is enabled by a reduction to a mixed-integer quadratic program.
Empirically, automatically choosing an estimator performs comparably to the best estimator chosen with hindsight.

assistant: text: We propose a gradient estimator selection algorithm with the aim on improving optimization efficiency.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS

Paper content:
A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness.
Yet Breiman shows a dilemma (Breiman, 1999) that a uniform improvement on margin distribution \emph{does not} necessarily reduces generalization error.
In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed normalized margins using Lipschitz constant bound by spectral norm products.
With both simplified theory and extensive experiments, Breiman's dilemma is shown to rely on dynamics of normalized margin distributions, that reflects the trade-off between model expression power and data complexity.
When the complexity of data is comparable to the model expression power in the sense that training and test data share similar phase transitions in normalized margin dynamics, two efficient ways are derived via classic margin-based generalization bounds to successfully predict the trend of generalization error.
On the other hand, over-expressed models that exhibit uniform improvements on training normalized margins may lose such a prediction power and fail to prevent the overfitting.

assistant: text: Bregman's dilemma is shown in deep learning that improvement of margins of over-parameterized models may result in overfitting, and dynamics of normalized margin distributions are proposed to predict generalization error and identify such a dilemma.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Faster and Just As Accurate: A Simple Decomposition for Transformer Models

Paper content:
Large pre-trained Transformers such as BERT have been tremendously effective for many NLP tasks.
However, inference in these large-capacity models is prohibitively slow and expensive
. Transformers are essentially a stack of self-attention layers which encode each input position using the entire input sequence as its context
. However, we find that it may not be necessary to apply this expensive sequence-wide self-attention over at all layers
. Based on this observation, we propose a decomposition to a pre-trained Transformer that allows the lower layers to process segments of the input independently enabling parallelism and caching
. We show that the information loss due to this decomposition can be recovered in the upper layers with auxiliary supervision during fine-tuning
.  We evaluate de-composition with pre-trained BERT models on five different paired-input tasks in question answering, sentence similarity, and natural language inference
.  Results show that decomposition enables faster inference (up to 4x), significant memory reduction (up to 70%) while retaining most (up to 99%) of the original performance
. We will release the code at<anonymized url>.

assistant: text: Inference in large Transformers is expensive due to the self-attention in multiple layers. We show a simple decomposition technique can yield a faster, low memory-footprint model that is just as accurate of the original models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to learn to communicate

Paper content:
How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment?
We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop.
Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3).
An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.
In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1).
We call our approach Learning to Learn to Communicate (L2C).
Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol.
Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans.
To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agents
trained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.

assistant: text: We propose to use meta-learning for more efficient language learning, via a kind of 'domain randomization'.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ergodic Inference: Accelerate Convergence by Optimisation

Paper content:
Statistical inference methods are fundamentally important in machine learning.
Most state-of-the-art inference algorithms are
variants of Markov chain Monte Carlo (MCMC) or variational inference (VI).
However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias.
In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation.
The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency.
We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.

assistant: text: In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using GANs for Generation of Realistic City-Scale Ride Sharing/Hailing Data Sets

Paper content:
This paper focuses on the synthetic generation of human mobility data in urban areas.
We present a novel and scalable application of Generative Adversarial Networks (GANs) for modeling and generating human mobility data.
We leverage actual ride requests from ride sharing/hailing services from four major cities in the US to train our GANs model.
Our model captures the spatial and temporal variability of the ride-request patterns observed for all four cities on any typical day and over any typical week.
Previous works have succinctly characterized the spatial and temporal properties of human mobility data sets using the fractal dimensionality and the densification power law, respectively, which we utilize to validate our GANs-generated synthetic data sets.
Such synthetic data sets can avoid privacy concerns and be extremely useful for researchers and policy makers on urban mobility and intelligent transportation.

assistant: text: This paper focuses on the synthetic generation of human mobility data in urban areas using GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Enhancing Language Emergence through Empathy

Paper content:
The emergence of language in multi-agent settings is a promising research direction to ground natural language in simulated agents.
If AI would be able to understand the meaning of language through its using it, it could also transfer it to other situations flexibly.
That is seen as an important step towards achieving general AI.
The scope of emergent communication is so far, however, still limited.
It is necessary to enhance the learning possibilities for skills associated with communication to increase the emergable complexity.
We took an example from human language acquisition and the importance of the empathic connection in this process.
We propose an approach to introduce the notion of empathy to multi-agent deep reinforcement learning.
We extend existing approaches on referential games with an auxiliary task for the speaker to predict the listener's mind change improving the learning time.
Our experiments show the high potential of this architectural element by doubling the learning speed of the test setup.

assistant: text: An auxiliary prediction task can speed up learning in language emergence setups.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantum Graph Neural Networks

Paper content:
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network.
Along with this general class of ansatze, we introduce further specialized architectures, namely, Quantum Graph Recurrent Neural Networks (QGRNN) and Quantum Graph Convolutional Neural Networks (QGCNN).
We provide four example applications of QGNN's: learning Hamiltonian dynamics of quantum systems, learning how to create multipartite entanglement in a quantum network, unsupervised learning for spectral clustering, and supervised learning for graph isomorphism classification.

assistant: text: Introducing a new class of quantum neural networks for learning graph-based representations on quantum computers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks

Paper content:
In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network, significantly extending prior work on a method known as ``Bayesian Dark Knowledge.
"  Our generalized framework applies to the case of classification models and takes as input the architecture of a ``teacher" network, a general posterior expectation of interest, and the architecture of a ``student" network.
The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples from the parameter posterior of the teacher model.
We further consider the problem of optimizing the student model architecture with respect to an accuracy-speed-storage trade-off.
We present experimental results investigating multiple data sets, distillation targets,  teacher model architectures, and approaches to searching for student model architectures.
We establish the key result that distilling into a student model with an architecture that matches the teacher, as is done in Bayesian Dark Knowledge, can lead to sub-optimal performance.
Lastly, we show that student architecture search methods can identify student models with significantly improved performance.

assistant: text: A general framework for distilling Bayesian posterior expectations for deep neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Going Deeper with Lean Point Networks

Paper content:
We introduce three generic point cloud processing blocks that improve both accuracy and memory consumption of multiple state-of-the-art networks, thus allowing to design deeper and more accurate networks.
The novel processing blocks that facilitate efficient information flow are a convolution-type operation block for point sets that blends neighborhood information in a memory-efficient manner; a multi-resolution point cloud processing block; and a crosslink block that efficiently shares information across low- and high-resolution processing branches.
Combining these blocks, we design significantly wider and deeper architectures.
We extensively evaluate the proposed architectures on multiple point segmentation benchmarks (ShapeNetPart, ScanNet, PartNet) and report systematic improvements in terms of both accuracy and memory consumption by using our generic modules in conjunction with multiple recent architectures (PointNet++, DGCNN, SpiderCNN, PointCNN).
We report a 9.7% increase in IoU on the PartNet dataset, which is the most complex, while decreasing memory footprint  by  57%.

assistant: text: We introduce three generic point cloud processing blocks that improve both accuracy and memory consumption of multiple state-of-the-art networks, thus allowing to design deeper and more accurate networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sequential Coordination of Deep Models for Learning Visual Arithmetic

Paper content:
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive.
Consider a visual arithmetic task, where the goal is to carry out simple arithmetical algorithms on digits presented under natural conditions (e.g. hand-written, placed randomly).
We propose a two-tiered architecture for tackling this kind of problem.
The lower tier consists of a heterogeneous collection of information processing modules, which can include pre-trained deep neural networks for locating and extracting characters from the image, as well as modules performing symbolic transformations on the representations extracted by perception.
The higher tier consists of a controller, trained using reinforcement learning, which coordinates the modules in order to solve the high-level task.
For instance, the controller may learn in what contexts to execute the perceptual networks and what symbolic transformations to apply to their outputs.
The resulting model is able to solve a variety of tasks in the visual arithmetic domain,and has several advantages over standard, architecturally homogeneous feedforward networks including improved sample efficiency.

assistant: text: We use reinforcement learning to train an agent to solve a set of visual arithmetic tasks using provided pre-trained perceptual modules and transformations of internal representations created by those modules.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Audio Adversarial Examples Using Temporal Dependency

Paper content:
Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream  applications.
Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs.
In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples.
Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that
(i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks;
(ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments.
Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.

assistant: text: Adversarial audio discrimination using temporal dependency
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Calibration of neural network logit vectors to combat adversarial attacks

Paper content:
Adversarial examples remain an issue for contemporary neural networks.
This paper draws on Background Check (Perello-Nieto et al., 2016), a technique in model calibration, to assist two-class neural networks in detecting adversarial examples, using the one dimensional difference between logit values as the underlying measure.
This method interestingly tends to achieve the highest average recall on image sets that are generated with large perturbation vectors, which is unlike the existing literature on adversarial attacks (Cubuk et al., 2017).
The proposed method does not need knowledge of the attack parameters or methods at training time, unlike a great deal of the literature that uses deep learning based methods to detect adversarial examples, such as Metzen et al. (2017), imbuing the proposed method with additional flexibility.

assistant: text: This paper uses principles from the field of calibration in machine learning on the logits of a neural network to defend against adversarial attacks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration

Paper content:
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model.
The proposed control problem contains a restoration dynamics which is modeled by an RNN.
The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network.
We call the proposed model the dynamically unfolding recurrent restorer (DURR).
Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking.
Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.

assistant: text: We propose a novel method to handle image degradations of different levels by learning a diffusion terminal time. Our model can generalize to unseen degradation level and different noise statistic.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visualizing Music Transformer

Paper content:
Like language, music can be represented as a sequence of discrete symbols that form a hierarchical syntax, with notes being roughly like characters and motifs of notes like words.
Unlike text however, music relies heavily on repetition on multiple timescales to build structure and meaning.
The Music Transformer has shown compelling results in generating music with structure (Huang et al., 2018).
In this paper, we introduce a tool for visualizing self-attention on polyphonic music with an interactive pianoroll.
We use music transformer as both a descriptive tool and a generative model.
For the former, we use it to analyze existing music to see if the resulting self-attention structure corroborates with the musical structure known from music theory.
For the latter, we inspect the model's self-attention during generation, in order to understand how past notes affect future ones.
We also compare and contrast the attention structure of regular attention to that of relative attention (Shaw et al., 2018, Huang et al., 2018), and examine its impact on the resulting generated music.
For example, for the JSB Chorales dataset, a model trained with relative attention is more consistent in attending to all the voices in the preceding timestep and the chords before, and at cadences to the beginning of a phrase, allowing it to create an arc.
We hope that our analyses will offer more evidence for relative self-attention as a powerful inductive bias for modeling music.
We invite the reader to explore our video animations of music attention and to interact with the visualizations at https://storage.googleapis.com/nips-workshop-visualization/index.html.

assistant: text: Visualizing the differences between regular and relative attention for Music Transformer.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: End-to-end learning of energy-based representations for irregularly-sampled signals and images

Paper content:
For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often irregular space-time sampling patterns and large missing data rates.
These sampling properties is a critical issue to apply state-of-the-art learning-based (e.g., auto-encoders, CNNs,...) to fully benefit from the available large-scale observations and reach breakthroughs in the reconstruction and identification of processes of interest.
In this paper, we address the end-to-end learning of representations of signals, images and image sequences from irregularly-sampled data, {\em i.e.} when the training data involved missing data.
From an analogy to Bayesian formulation, we consider energy-based representations.
Two energy forms are investigated: one derived from auto-encoders and one relating to Gibbs energies.
The learning stage of these energy-based representations (or priors) involve a joint interpolation issue, which resorts to solving an energy minimization problem under observation constraints.
Using a neural-network-based implementation of the considered energy forms, we can state an end-to-end learning scheme from irregularly-sampled data.
We demonstrate the relevance of the proposed representations for different case-studies: namely, multivariate time series, 2{\sc } images and image sequences.

assistant: text: We address the end-to-end learning of energy-based representations for signal and image observation dataset with irregular sampling patterns.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Memory-efficient Learning for Large-scale Computational Imaging

Paper content:
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks).
However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units.
In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.
We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.

assistant: text: We propose a memory-efficient learning procedure that exploits the reversibility of the network’s layers to enable data-driven design for large-scale computational imaging.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Natural Tendency of Feed Forward Neural Networks to Favor Invariant Units

Paper content:
A central goal in the study of the primate visual cortex and hierarchical models for object recognition is understanding how and why single units trade off invariance versus sensitivity to image transformations.
For example, in both deep networks and visual cortex there is substantial variation from layer-to-layer and unit-to-unit in the degree of translation invariance.
Here, we provide theoretical insight into this variation and its consequences for encoding in a deep network.
Our critical insight comes from the fact that rectification simultaneously decreases response variance and correlation across responses to transformed stimuli, naturally inducing a positive relationship between invariance and dynamic range.
Invariant input units then tend to drive the network more than those sensitive to small image transformations.
We discuss consequences of this relationship for AI: deep nets naturally weight invariant units over sensitive units, and this can be strengthened with training, perhaps contributing to generalization performance.
Our results predict a signature relationship between invariance and dynamic range that can now be tested in future neurophysiological studies.

assistant: text: Rectification in deep neural networks naturally leads them to favor an invariant representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tracking momentary attention fluctuations with an EEG-based cognitive brain-machine interface

Paper content:
Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas.
Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain.
We investigate this link using a real-time cognitive brain machine interface (cBMI) based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials evoked by rhythmically flashing stimuli.
Tracking momentary fluctuations in SSVEP power, in real-time, we presented stimuli time-locked to when this power reached (predetermined) high or low thresholds.
We observed a significant increase in discrimination accuracy (d') when stimuli were triggered during high (versus low) SSVEP power epochs, at the location cued for attention.
Our results indicate a direct link between attention’s effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain.

assistant: text: With a cognitive brain-machine interface, we show a direct link between attentional effects on perceptual accuracy and neural gain in EEG-SSVEP power, in the human brain.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

Paper content:
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.
In this paper,we connect a general family of learning objectives including score matching to Wassersteingradient flows.
This connection enables us to design a scalable approximation to theseobjectives, with a form similar to single-step contrastive divergence.
We present applications in training implicit variational and Wasserstein auto-encoders with manifold-valued priors.

assistant: text: We present a scalable approximation to a wide range of EBM objectives, and applications in implicit VAEs and WAEs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks 

Paper content:
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.
Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of the minimal deterministic finite automaton (MDFA) for the language.
Our experiments show that such a decoding function indeed exists, and that it maps states of the RNN not to MDFA states, but to states of an {\em abstraction} obtained by clustering small sets of MDFA states into ``''superstates''.
A qualitative analysis reveals that the abstraction often has a simple interpretation.
Overall, the results suggest a strong structural relationship between internal representations used by RNNs and finite automata, and explain the well-known ability of RNNs to recognize formal grammatical structure.

assistant: text: Finite Automata Can be Linearly decoded from Language-Recognizing RNNs using low coarseness abstraction functions and high accuracy decoders.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learn to Explain Efficiently via Neural Logic Inductive Learning

Paper content:
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.
In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP).
We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data.
In experiments, compared with the state-of-the-art models, we find NLIL is able to search for rules that are x10 times longer while remaining x3 times faster.
We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.
In this work, we propose Neural Logic Inductive Learning, a differentiable ILP framework that learns explanatory rules from data.
We demonstrate that NLIL can scale to very large datasets while being able to search over complex and expressive rules.
More importantly, we show that a scalable ILP method is effective in explaining decisions of supervised models, which provides an alternative perspective for inspecting the decision process of machine learning systems.

assistant: text: An efficient differentiable ILP model that learns first-order logic rules that can explain the data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explanation-Based Attention for Semi-Supervised Deep Active Learning

Paper content:
We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
The proposed attention mechanism is based on recent methods to visually explain predictions made by DNNs.
We apply the proposed explanation-based attention to MNIST and SVHN classification.
The conducted experiments show accuracy improvements for the original and class-imbalanced datasets with the same number of training examples and faster long-tail convergence compared to uncertainty-based methods.

assistant: text: We introduce an attention mechanism to improve feature extraction for deep active learning (AL) in the semi-supervised setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: In Support of Over-Parametrization in Deep Reinforcement Learning: an Empirical Study

Paper content:
There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error.
In other words, the bias-variance tradeoff is not directly observable when increasing network width arbitrarily.
We investigate whether a corresponding phenomenon is present in reinforcement learning.
We experiment on four OpenAI Gym environments, increasing the width of the value and policy networks beyond their prescribed values.
Our empirical results lend support to this hypothesis.
However, tuning the hyperparameters of each network width separately remains as important future work in environments/algorithms where the optimal hyperparameters vary noticably across widths, confounding the results when the same hyperparameters are used for all widths.

assistant: text: Over-parametrization in width seems to help in deep reinforcement learning, just as it does in supervised learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving Search Through A3C Reinforcement Learning Based Conversational Agent

Paper content:
We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent.
Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities.
Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming.
We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent.
We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user.
We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes.
Our experiments show that the agent learns to achieve higher rewards and better states.

assistant: text: A Reinforcement Learning based conversational search assistant which provides contextual assistance in subjective search (like digital assets).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Wasserstein-Bounded Generative Adversarial Networks

Paper content:
In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.
Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes.
In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term.
Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection.
Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.

assistant: text: Propose an improved framework for WGANs and demonstrate its better performance in theory and practice.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Paper content:
In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification.
However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images.
We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks.
Defense-GAN is trained to model the distribution of unperturbed images.
At inference time, it finds a close output to a given image which does not contain the adversarial changes.
This output is then fed to the classifier.
Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure.
It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples.
We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.

assistant: text: Defense-GAN uses a Generative Adversarial Network to defend against white-box and black-box attacks in classification models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Random Splines for Point Process Intensity Estimation

Paper content:
Gaussian processes are the leading class of distributions on random functions, but they suffer from well known issues including difficulty scaling and inflexibility with respect to certain shape constraints (such as nonnegativity).
Here we propose Deep Random Splines, a flexible class of random functions obtained by transforming Gaussian noise through a deep neural network whose output are the parameters of a spline.
Unlike Gaussian processes, Deep Random Splines allow us to readily enforce shape constraints while inheriting the richness and tractability of deep generative models.
We also present an observational model for point process data which uses Deep Random Splines to model the intensity function of each point process and apply it to neuroscience data to obtain a low-dimensional representation of spiking activity.
Inference is performed via a variational autoencoder that uses a novel recurrent encoder architecture that can handle multiple point processes as input.

assistant: text: We combine splines with neural networks to obtain a novel distribution over functions and use it to model intensity functions of point processes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarial Spheres

Paper content:
State of the art computer vision models have been shown to be vulnerable to small adversarial perturbations of the input.
In other words, most images in the data distribution are both correctly classified by the model and are very close to a visually similar misclassified image.
Despite substantial research interest, the cause of the phenomenon is still poorly understood and remains unsolved.
We hypothesize that this counter intuitive behavior is a naturally occurring result of the high dimensional geometry of the data manifold.
As a first step towards exploring this hypothesis, we study a simple synthetic dataset of classifying between two concentric high dimensional spheres.
For this dataset we show a fundamental tradeoff between the amount of test error and the average distance to nearest error.
In particular, we prove that any model which misclassifies a small constant fraction of a sphere will be vulnerable to adversarial perturbations of size $O(1/\sqrt{d})$.
Surprisingly, when we train several different architectures on this dataset, all of their error sets naturally approach this theoretical bound.
As a result of the theory, the vulnerability of neural networks to small adversarial perturbations is a logical consequence of the amount of test error observed.
We hope that our theoretical analysis of this very simple case will point the way forward to explore how the geometry of complex real-world data sets leads to adversarial examples.

assistant: text: We hypothesize that the vulnerability of image models to small adversarial perturbation is a naturally occurring result of the high dimensional geometry of the data manifold. We explore and theoretically prove this hypothesis for a simple synthetic dataset.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Synthesizing Robust Adversarial Examples

Paper content:
Neural network-based classifiers parallel or exceed human-level accuracy on many common tasks and are used in practical systems.
Yet, neural networks are susceptible to adversarial examples, carefully perturbed inputs that cause networks to misbehave in arbitrarily chosen ways.
When generated with standard methods, these examples do not consistently fool a classifier in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations.
Adversarial examples generated using standard techniques require complete control over direct input to the classifier, which is impossible in many real-world systems.
We introduce the first method for constructing real-world 3D objects that consistently fool a neural network across a wide distribution of angles and viewpoints.
We present a general-purpose algorithm for generating adversarial examples that are robust across any chosen distribution of transformations.
We demonstrate its application in two dimensions, producing adversarial images that are robust to noise, distortion, and affine transformation.
Finally, we apply the algorithm to produce arbitrary physical 3D-printed adversarial objects, demonstrating that our approach works end-to-end in the real world.
Our results show that adversarial examples are a practical concern for real-world systems.

assistant: text: We introduce a new method for synthesizing adversarial examples robust in the physical world and use it to fabricate the first 3D adversarial objects.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Interpretable Evaluations: A Case Study of Named Entity Recognition

Paper content:
With the proliferation of models for natural language processing (NLP) tasks, it is even harder to understand the differences between models and their relative merits.
Simply looking at differences between holistic metrics such as accuracy, BLEU, or F1 do not tell us \emph{why} or \emph{how} a particular method is better and how dataset biases influence the choices of model design.
In this paper, we present a general methodology for {\emph{interpretable}} evaluation of NLP systems and choose the task of named entity recognition (NER) as a case study, which is a core task of identifying people, places, or organizations in text.
The proposed evaluation method enables us to interpret the \textit{model biases}, \textit{dataset biases}, and how the \emph{differences in the datasets} affect the design of the models, identifying the strengths and weaknesses of current approaches.
By making our {analysis} tool available, we make it easy for future researchers to run similar analyses and drive the progress in this area.

assistant: text: We propose a generalized evaluation methodology to interpret model biases, dataset biases, and their correlation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Large Scale GAN Training for High Fidelity Natural Image Synthesis

Paper content:
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.
To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale.
We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick", allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input.
Our modifications lead to models which set the new state of the art in class-conditional image synthesis.
When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Frechet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.

assistant: text: GANs benefit from scaling up.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning

Paper content:
Deep neural networks (DNNs) typically have enough capacity to fit random data by brute force even when conventional data-dependent regularizations focusing on the geometry of the features are imposed.
We find out that the reason for this is the inconsistency between the enforced geometry and the standard softmax cross entropy loss.
To resolve this, we propose a new framework for data-dependent DNN regularization, the Geometrically-Regularized-Self-Validating neural Networks (GRSVNet).
During training, the geometry enforced on one batch of features is simultaneously validated on a separate batch using a validation loss consistent with the geometry.
We study  a particular case of GRSVNet, the Orthogonal-Low-rank Embedding (OLE)-GRSVNet, which is capable of producing highly discriminative features residing in orthogonal low-rank subspaces.
Numerical experiments show that OLE-GRSVNet outperforms DNNs with conventional regularization when trained on real data.
More importantly, unlike conventional DNNs, OLE-GRSVNet refuses to memorize random data or random labels, suggesting it only learns intrinsic patterns by reducing the memorizing capacity of the baseline DNN.

assistant: text: we propose a new framework for data-dependent DNN regularization that can prevent DNNs from overfitting random data or random labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Connecting the Dots Between MLE and RL for Sequence Prediction

Paper content:
Sequence prediction models can be learned from example sequences with a variety of training algorithms.
Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time.
Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency.
A rich set of other algorithms, such as data noising, RAML, and softmax policy gradient, have also been developed from different perspectives.
In this paper, we present a formalism of entropy regularized policy optimization, and show that the apparently distinct algorithms, including MLE, can be reformulated as special instances of the formulation.
The difference between them is characterized by the reward function and two weight hyperparameters.
The unifying interpretation enables us to systematically compare the algorithms side-by-side, and gain new insights into the trade-offs of the algorithm design.
The new perspective also leads to an improved approach that dynamically interpolates among the family of algorithms, and learns the model in a scheduled way.
Experiments on machine translation, text summarization, and game imitation learning demonstrate superiority of the proposed approach.

assistant: text: An entropy regularized policy optimization formalism subsumes a set of sequence prediction learning algorithms. A new interpolation algorithm with improved results on text generation and game imitation learning.