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

Title: Learning to Defense by Learning to Attack

Paper content:
Adversarial training provides a principled approach for training robust neural networks.
From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem.
The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to generate adversarial samples.
Unfortunately, such a minmax problem is very difficult to solve due to the lack of convex-concave structure.
This work proposes a new adversarial training method based on a general learning-to-learn framework.
Specifically, instead of applying the existing hand-design algorithms for the inner problem, we learn an optimizer, which is parametrized as a convolutional neural network.
At the same time, a robust classifier is learned to defense the adversarial attack generated by the learned optimizer.
From the perspective of generative learning, our proposed method can be viewed as learning a deep generative model for generating adversarial samples, which is adaptive to the robust classification.
Our experiments demonstrate that our proposed method significantly outperforms existing adversarial training methods on CIFAR-10 and CIFAR-100 datasets.

assistant: text: This paper proposes using the learning-to-learn framework to learn an attacker.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Estimating Information Flow in DNNs

Paper content:
We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory.
The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases.
Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X).
This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works.
To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters.
This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations.
We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.
By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class.
Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space.
Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering.
This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

assistant: text: This paper provides a principled way to examine the compression phrase in deep neural networks by providing an theoretical sounding entropy estimator to estimate mutual information.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automated Science Scheduling for the ECOSTRESS Mission

Paper content:
We describe the use of an automated scheduling system for observation policy design and to schedule operations of the NASA (National Aeronautics and Space Administration) ECOSystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS).
We describe the adaptation of the Compressed Large-scale Activity Scheduler and Planner (CLASP) scheduling system to the ECOSTRESS scheduling problem, highlighting multiple use cases for automated scheduling and several challenges for the scheduling technology: handling long-term campaigns with changing information, Mass Storage Unit Ring Buffer operations challenges, and orbit uncertainty.
The described scheduling system has been used for operations of the ECOSTRESS instrument since its nominal operations start July 2018 and is expected to operate until mission end in Summer 2019.

assistant: text: This paper presents an adaptation of an automated scheduling system, CLASP, to target an EO experiment (ECOSTRESS) on the ISS.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The divergences minimized by non-saturating GAN training

Paper content:
Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been
theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting
extensions such as f-GANs and Wasserstein GANs.
For both classic GANs and f-GANs, there is an original variant of training and a "non-saturating" variant which uses an alternative form of generator gradient.
The original variant is theoretically easier to study, but for GANs the alternative variant performs better in practice.
The non-saturating scheme is often regarded as a simple modification to deal with optimization issues, but we show that in fact the non-saturating scheme for GANs is effectively optimizing a reverse KL-like f-divergence.
We also develop a number of theoretical tools to help compare and classify f-divergences.
We hope these results may help to clarify some of the theoretical discussion surrounding the divergence minimization view of GAN training.

assistant: text: This paper proposes a useful expression of the class of f-divergences, investigates theoretical properties of popular f-divergences from newly developed tools, and investigates GANs with the non-saturating training scheme.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Monotonic Chunkwise Attention

Paper content:
Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.
To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.
We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.
When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.
In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.

assistant: text: An online and linear-time attention mechanism that performs soft attention over adaptively-located chunks of the input sequence.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS

Paper content:
We consider the problem of topic modeling in a weakly semi-supervised setting.
In this scenario, we assume that the user knows a priori a subset of the topics she wants the model to learn and is able to provide a few exemplar documents for those topics.
In addition, while each document may typically consist of multiple topics, we do not assume that the user will identify all its topics exhaustively.
Recent state-of-the-art topic models such as NVDM, referred to herein as Neural Topic Models (NTMs), fall under the variational autoencoder framework.
We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.
After analyzing the effect of informative priors, we propose a simple modification of the NVDM model using a logit-normal posterior that we show achieves better alignment to user-desired topics versus other NTM models.

assistant: text: We propose supervising VAE-style topic models by intelligently adjusting the prior on a per document basis. We find a logit-normal posterior provides the best performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NEURAL EXECUTION ENGINES

Paper content:
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.
There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.
We study this problem through the lens of fundamental computer science problems: sorting and graph processing.
We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.
We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.
Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.

assistant: text: We propose neural execution engines (NEEs), which leverage a learned mask and supervised execution traces to mimic the functionality of subroutines and demonstrate strong generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Paper content:
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.
The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.
In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth.
To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training.
We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus.
On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB.
Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.
Large-scale distributed training improves the productivity of training deeper and larger models BID7 BID35 BID24 BID37 .
Synchronous stochastic gradient descent (SGD) is widely used for distributed training.
By increasing the number of training nodes and taking advantage of data parallelism, the total computation time of the forward-backward passes on the same size training data can be dramatically reduced.
However, gradient exchange is costly and dwarfs the savings of computation time

assistant: text: This paper proposes additional improvement over gradient dropping to improve communication efficiency
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Softmax Supervision with Isotropic Normalization

Paper content:
The softmax function is widely used to train deep neural networks for multi-class classification.
Despite its outstanding performance in classification tasks, the features derived from the supervision of softmax are usually sub-optimal in some scenarios where Euclidean distances apply in feature spaces.
To address this issue, we propose a new loss, dubbed the isotropic loss, in the sense that the overall distribution of data points is regularized to approach the isotropic normal one.
Combined with the vanilla softmax, we formalize a novel criterion called the isotropic softmax, or isomax for short, for supervised learning of deep neural networks.
By virtue of the isomax, the intra-class features are penalized by the isotropic loss while inter-class distances are well kept by the original softmax loss.
Moreover, the isomax loss does not require any additional modifications to the network, mini-batches or the training process.
Extensive experiments on classification and clustering are performed to demonstrate the superiority and robustness of the isomax loss.

assistant: text: The discriminative capability of softmax for learning feature vectors of objects is effectively enhanced by virture of isotropic normalization on global distribution of data points.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ACIQ: Analytical Clipping for Integer Quantization of neural networks

Paper content:
We analyze the trade-off between quantization noise and clipping distortion in low precision networks.
We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping.
By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping.
For example, just by choosing the accurate clipping values, more than 40\% accuracy improvement is obtained for the quantization of VGG-16 to 4-bits of precision.
Our results have many applications for the quantization of neural networks at both training and inference time.

assistant: text: We analyze the trade-off between quantization noise and clipping distortion in low precision networks, and show marked improvements over standard quantization schemes that normally avoid clipping
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP

Paper content:
A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.
Recently,  Jin et al. (2018) proposed a Q-learning algorithm with UCB exploration policy, and proved it has nearly optimal regret bound for finite-horizon episodic MDP.
In this paper, we adapt Q-learning with UCB-exploration bonus to infinite-horizon MDP with discounted rewards \emph{without} accessing a generative model.
We show that the \textit{sample complexity of exploration} of our algorithm is bounded by $\tilde{O}({\frac{SA}{\epsilon^2(1-\gamma)^7}})$.
This improves the previously best known result of $\tilde{O}({\frac{SA}{\epsilon^4(1-\gamma)^8}})$ in this setting achieved by delayed Q-learning (Strehlet al., 2006),, and matches the lower bound in terms of $\epsilon$ as well as $S$ and $A$ up to logarithmic factors.

assistant: text: This paper considered a Q-learning algorithm with UCB exploration policy for infinite-horizon MDP.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Missing Information in Deep Networks Using Backpropagated Gradients

Paper content:
Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data.
We attribute this vulnerability to the limitations inherent to activation-based representation.
To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.
In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information.
To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations.
We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes.
Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets.
The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.

assistant: text: The authors present creating representations based on gradients with respect to the weights to supplement information missing from the training dataset for deep networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Characterizing Sparse Connectivity Patterns in Neural Networks

Paper content:
We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training.
Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than 0.5% classification layer connection density, or less than 5% overall network connection density.
We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers.
Based on our sparsifying technique, we introduce the `scatter' metric to characterize the quality of a particular connection pattern.
As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.

assistant: text: Proposal to reduce the number of parameters learned by a deep network by setting up sparse connection weights in classiication layers, and introduction of a concept of "scatter."
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Function changes in the Backpropagation equation is equivalent to an implicit learning rate

Paper content:
The backpropagation algorithm is the de-facto standard for credit assignment in artificial neural networks due to its empirical results.
Since its conception, variants of the backpropagation algorithm have emerged.
More specifically, variants that leverage function changes in the backpropagation equations to satisfy their specific requirements.
Feedback Alignment is one such example, which replaces the weight transpose matrix in the backpropagation equations with a random matrix in search of a more biologically plausible credit assignment algorithm.
In this work, we show that function changes in the  backpropagation procedure is equivalent to adding an implicit learning rate to an artificial neural network.
Furthermore, we learn activation function derivatives in the backpropagation equations to demonstrate early convergence in these artificial neural networks.
Our work reports competitive performances with early convergence on MNIST and CIFAR10 on sufficiently large deep neural network architectures.

assistant: text: We demonstrate that function changes in the backpropagation is equivalent to an implicit learning rate
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Paper content:
Zeroth-order optimization is the process of minimizing an objective $f(x)$, given oracle access to evaluations at adaptively chosen inputs $x$.
In this paper, we present two simple yet powerful GradientLess Descent (GLD) algorithms that do not rely on an underlying gradient estimate and are numerically stable.
We analyze our algorithm from a novel geometric perspective and we show that for {\it any monotone transform} of a smooth and strongly convex objective with latent dimension $k \ge n$, we present a novel analysis that shows convergence within an $\epsilon$-ball of the optimum in $O(kQ\log(n)\log(R/\epsilon))$ evaluations, where the input dimension is $n$, $R$ is the diameter of the input space and $Q$ is the condition number.
Our rates are the first of its kind to be both
1) poly-logarithmically dependent on dimensionality and
2) invariant under monotone transformations.
We further leverage our geometric perspective to show that our analysis is optimal.
Both monotone invariance and its ability to utilize a low latent dimensionality are key to the empirical success of our algorithms, as demonstrated on synthetic and MuJoCo benchmarks.

assistant: text: Gradientless Descent is a provably efficient gradient-free algorithm that is monotone-invariant and fast for high-dimensional zero-th order optimization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Structural Fingerprints for Graph Attention Networks

Paper content:
Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction.
The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN's) for graph data processing.
Recently, graph attention network (GAT) has proven a promising attempt by combining graph neural networks with attention mechanism, so as to achieve massage passing in graphs with arbitrary structures.
However, the attention in GAT is computed mainly based on the similarity between the node content, while the structures of the graph remains largely unemployed (except in masking the attention out of one-hop neighbors).
In this paper, we propose an `````````````````````````````"ADaptive Structural Fingerprint" (ADSF) model to fully exploit both topological details of the graph and  content features of the nodes.
The key idea is to contextualize each node with a weighted, learnable receptive field  encoding rich and diverse local graph structures.
By doing this, structural interactions between the nodes can  be inferred accurately, thus improving subsequent attention layer as well as the convergence of learning.
Furthermore, our model provides a useful platform  for different subspaces of node features and various scales of graph structures to ``cross-talk'' with each other through the learning of multi-head attention, being particularly useful in handling complex real-world data.
Encouraging performance is observed on a number of benchmark data sets in node classification.

assistant: text: Exploiting rich strucural details in graph-structued data via adaptive "strucutral fingerprints''
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categories

Paper content:
We address the problem of learning to discover 3D parts for objects in unseen categories.
Being able to learn the geometry prior of parts and transfer this prior to unseen categories pose fundamental challenges on data-driven shape segmentation approaches.
Formulated as a contextual bandit problem, we propose a learning-based iterative grouping framework which learns a grouping policy to progressively merge small part proposals into bigger ones in a bottom-up fashion.
At the core of our approach is to restrict the local context for extracting part-level features, which encourages the generalizability to novel categories.
On a recently proposed large-scale fine-grained 3D part dataset, PartNet, we demonstrate that our method can transfer knowledge of parts learned from 3 training categories to 21 unseen testing categories without seeing any annotated samples.
Quantitative comparisons against four strong shape segmentation baselines show that we achieve the state-of-the-art performance.

assistant: text: This paper proposes a method for part segmentation in object point clouds.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Amortized Bayesian Meta-Learning

Paper content:
Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data.
State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the meta learner can generalize to an evaluation episode quickly.
These methods perform well but often lack good quantification of uncertainty, which can be vital to real-world applications when data is lacking.
We propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of Bayes by Backprop will produce a good task-specific approximate posterior.
We show that our method produces good uncertainty estimates on contextual bandit and few-shot learning benchmarks.

assistant: text: We propose a meta-learning method which efficiently amortizes hierarchical variational inference across training episodes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Residual Non-local Attention Networks for Image Restoration

Paper content:
In this paper, we propose a residual non-local attention network for high-quality image restoration.
Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features.
To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts.
Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block.
The trunk branch is used to extract hierarchical features.
Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions.
The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map.
Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network.
Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution.
Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.

assistant: text: This paper proposes a residual non-local attention network for image restoration
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ternary MobileNets via Per-Layer Hybrid Filter Banks

Paper content:
MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years.
New applications with stringent real-time requirements in highly constrained devices require further compression of MobileNets-like already computeefficient networks.
Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 − 6 bits albeit with a modest to significant drop in accuracy.
While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs.
Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets.
The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets.
Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.

assistant: text: Focuses on quantizing the MobileNets architecture to ternary values, lowering the required space and computation in order to make neural networks more energy efficient.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Infinite-horizon Off-Policy Policy Evaluation with Multiple Behavior Policies

Paper content:
We consider off-policy policy evaluation when the trajectory data are generated by multiple behavior policies.
Recent work has shown the key role played by the state or state-action stationary distribution corrections in the infinite horizon context for off-policy policy evaluation.
We propose estimated mixture policy (EMP), a novel class of partially policy-agnostic methods to accurately estimate those quantities.
With careful analysis, we show that EMP gives rise to estimates with reduced variance for estimating the state stationary distribution correction while it also offers a useful induction bias for estimating the state-action stationary distribution correction.
In extensive experiments with both continuous and discrete environments, we demonstrate that our algorithm offers significantly improved accuracy compared to the state-of-the-art methods.

assistant: text: A new partially policy-agnostic method for infinite-horizon off-policy policy evalution with multiple known or unknown behavior policies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency

Paper content:
Image-to-image translation has recently received significant attention due to advances in deep learning.
Most works focus on learning either a one-to-one mapping in an unsupervised way or a many-to-many mapping in a supervised way.
However, a more practical setting is many-to-many mapping in an unsupervised way, which is harder due to the lack of supervision and the complex inner- and cross-domain variations.
To alleviate these issues, we propose the Exemplar Guided & Semantically Consistent Image-to-image Translation (EGSC-IT) network which conditions the translation process on an exemplar image in the target domain.
We assume that an image comprises of a content component which is shared across domains, and a style component specific to each domain.
Under the guidance of an exemplar from the target domain we apply Adaptive Instance Normalization to the shared content component, which allows us to transfer the style information of the target domain to the source domain.
To avoid semantic inconsistencies during translation that naturally appear due to the large inner- and cross-domain variations, we introduce the concept of feature masks that provide coarse semantic guidance without requiring the use of any semantic labels.
Experimental results on various datasets show that EGSC-IT does not only translate the source image to diverse instances in the target domain, but also preserves the semantic consistency during the process.

assistant: text: The paper explores the idea that an image has two components and applies an attention model where the feature masks that steer the translation process do not require semantic labels
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Size-free generalization bounds for convolutional neural networks

Paper content:
We prove bounds on the generalization error of convolutional networks.
The bounds are in terms of the training loss, the number of
parameters, the Lipschitz constant of the loss and the distance from
the weights to the initial weights.
They are independent of the
number of pixels in the input, and the height and width of hidden
feature maps.
We present experiments with CIFAR-10, along with varying
hyperparameters of a deep convolutional network, comparing our bounds
with practical generalization gaps.

assistant: text: This paper presents a generalization bound for convolutional neural networks based on the number of parameters, the Lipschitz constant, and the distance of the final weights from initialization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: What’s in a functional brain parcellation?

Paper content:
To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain.
Here we consider atlases used to parcellate the brain when studying brain function.
We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.

assistant: text: All functional brain parcellations are wrong, but some are useful
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Instance Hardness

Paper content:
We introduce dynamic instance hardness (DIH) to facilitate the training of machine learning models.
DIH is a property of each training sample and is computed as the running mean of the sample's instantaneous hardness as measured over the training history.
We use DIH to evaluate how well a model retains knowledge about each training sample over time.
We find that for deep neural nets (DNNs), the DIH of a sample in relatively early training stages reflects its DIH in later stages and as a result, DIH can be effectively used to reduce the set of training samples in future epochs.
Specifically, during each epoch, only samples with high DIH are trained (since they are historically hard) while samples with low DIH can be safely ignored.
DIH is updated each epoch only for the selected samples, so it does not require additional computation.
Hence, using DIH during training leads to an appreciable speedup.
Also, since the model is focused on the historically more challenging samples, resultant models are more accurate.
The above, when formulated as an algorithm, can be seen as a form of curriculum learning, so we call our framework DIH curriculum learning (or DIHCL).
The advantages of DIHCL, compared to other curriculum learning approaches, are: (1) DIHCL does not require additional inference steps over the data not selected by DIHCL in each epoch, (2) the dynamic instance hardness, compared to static instance hardness (e.g., instantaneous loss), is more stable as it integrates information over the entire training history up to the present time.
Making certain mathematical assumptions, we formulate the problem of DIHCL as finding a curriculum that maximizes a multi-set function $f(\cdot)$, and derive an approximation bound for a DIH-produced curriculum relative to the optimal curriculum.
Empirically, DIHCL-trained DNNs significantly outperform random mini-batch SGD and other recently developed curriculum learning methods in terms of efficiency, early-stage convergence, and final performance, and this is shown in training several state-of-the-art DNNs on 11 modern datasets.

assistant: text: New understanding of training dynamics and metrics of memorization hardness lead to efficient and provable curriculum learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding the (Un)interpretability of Natural Image Distributions Using Generative Models

Paper content:
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.
More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images.
We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions.
We perform sanity check experiments to provide evidence that these probabilities are reasonable.
However, we also show that density functions of natural images are difficult to interpret and thus limited in use.
We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images.

assistant: text: The authors try to estimate the probability distribution of the image with the help of GAN and develop a proper approximation to the PDFs in the latent space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Do Deep Neural Networks for Segmentation Understand Insideness?

Paper content:
Image segmentation aims at grouping pixels that belong to the same object or region.
At the heart of image segmentation lies the problem of determining whether a pixel is inside or outside a region, which we denote as the "insideness" problem.
Many Deep Neural Networks (DNNs) variants excel in segmentation benchmarks, but regarding insideness, they have not been well visualized or understood: What representations do DNNs use to address the long-range relationships of insideness?
How do architectural choices affect the learning of these representations?
In this paper, we take the reductionist approach by analyzing DNNs solving the insideness problem in isolation, i.e. determining the inside of closed (Jordan) curves.
We demonstrate analytically that state-of-the-art feed-forward and recurrent architectures can implement solutions of the insideness problem for any given curve.
Yet, only recurrent networks could  learn these general solutions when the training enforced a specific "routine" capable of breaking down the long-range relationships.
Our results highlights the need for new training strategies that decompose the learning into appropriate stages, and that lead to the general class of solutions necessary for DNNs to understand insideness.

assistant: text: DNNs for image segmentation can implement solutions for the insideness problem but only some recurrent nets could learn them with a specific type of supervision.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FigureQA: An Annotated Figure Dataset for Visual Reasoning

Paper content:
We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images.
The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.
We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection.
To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.
To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives.
In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements.
We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline.
Preliminary results indicate that the task poses a significant machine learning challenge.
We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

assistant: text: The paper introduces a new visual reasoning dataset called Figure-QA which consists of 140K figure images and 1.55M QA pairs, which can help in developing models that can extract useful information from visual representations of data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Imitation Learning from Visual Data with Multiple Intentions

Paper content:
Recent advances in learning from demonstrations (LfD) with deep neural networks have enabled learning complex robot skills that involve high dimensional perception such as raw image inputs.
LfD algorithms generally assume learning from single task demonstrations.
In practice, however, it is more efficient for a teacher to demonstrate a multitude of tasks without careful task set up, labeling, and engineering.
Unfortunately in such cases, traditional imitation learning techniques fail to represent the multi-modal nature of the data, and often result in sub-optimal behavior.
In this paper we present an LfD approach for learning multiple modes of behavior from visual data.
Our approach is based on a stochastic deep neural network (SNN), which represents the underlying intention in the demonstration as a stochastic activation in the network.
We present an efficient algorithm for training SNNs, and for learning with vision inputs, we also propose an architecture that associates the intention with a stochastic attention module.
We demonstrate our method on real robot visual object reaching tasks, and show that
it can reliably learn the multiple behavior modes in the demonstration data.
Video results are available at https://vimeo.com/240212286/fd401241b9.

assistant: text: A new sampling-based approach for inference in latent variable models that applies to multi-modal imitation learning and works better than deterministic neural networks and stochastic neural networks for a real visual robotics task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average

Paper content:
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.
To understand consistency regularization, we conceptually explore how loss geometry interacts with training procedures.
The consistency loss dramatically improves generalization performance over supervised-only training; however, we show that SGD struggles to converge on the consistency loss and continues to make large steps that lead to changes in predictions on the test data.
Motivated by these observations, we propose to train consistency-based methods with Stochastic Weight Averaging (SWA), a recent approach which averages weights along the trajectory of SGD with a modified learning rate schedule.
We also propose fast-SWA, which further accelerates convergence by averaging multiple points within each cycle of a cyclical learning rate schedule.
With weight averaging, we achieve the best known semi-supervised results on CIFAR-10 and CIFAR-100, over many different quantities of labeled training data.
For example, we achieve 5.0% error on CIFAR-10 with only 4000 labels, compared to the previous best result in the literature of 6.3%.

assistant: text: Consistency-based models for semi-supervised learning do not converge to a single point but continue to explore a diverse set of plausible solutions on the perimeter of a flat region. Weight averaging helps improve generalization performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multiplicative Interactions and Where to Find Them

Paper content:
We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated.
We begin by showing that such layers strictly enrich the representable function classes of neural networks.
We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required.
We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation.
Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.

assistant: text: Presents multiplicative interaction as a unified characterization for representing commonly used model architecture design components, showing empirical proof of superior performance on tasks like RL and sequence modeling.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Addressing the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts

Paper content:
The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context.
Lexical features are fed into the first layer and propagated through a deep network of hidden layers.
We argue that the need to represent and propagate lexical features in each layer limits the model’s capacity for learning and representing other information relevant to the task.
To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder.
This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states.
We show that the proposed modification yields consistent improvements on standard WMT translation tasks and reduces the amount of lexical information passed along the hidden layers.
We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior.

assistant: text: Equipping the transformer model with shortcuts to the embedding layer frees up model capacity for learning novel information.