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

Title: Learning to Write by Learning the Objective

Paper content:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.
Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.
We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.
In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.
Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.
Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.

assistant: text: This paper proposes to improve RNN language model generation using augmented objectives inspired by Grice's maxims of communication.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Paper content:
Recent improvements in large-scale language models have driven progress on automatic generation of syntactically and semantically consistent text for many real-world applications.
Many of these advances leverage the availability of large corpora.
While training on such corpora encourages the model to understand long-range dependencies in text, it can also result in the models internalizing the social biases present in the corpora.
This paper aims to quantify and reduce biases exhibited by language models.
Given a conditioning context (e.g. a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sensitive attributes (e.g. country names, occupations, genders, etc.) in the conditioning context, a.k.a. counterfactual evaluation.
We quantify these biases by adapting individual and group fairness metrics from the fair machine learning literature.
Extensive evaluation on two different corpora (news articles and Wikipedia) shows that state-of-the-art Transformer-based language models exhibit biases learned from data.
We propose embedding-similarity and sentiment-similarity regularization methods that improve both individual and group fairness metrics without sacrificing perplexity and semantic similarity---a positive step toward development and deployment of fairer language models for real-world applications.

assistant: text: This paper proposes to evaluate bias in pre-trained language models by using a fixed sentiment system and tests several different prefix templates.
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: The authors consider meta-learning to learn a prior over neural network weights, done via amortized variational inference.
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: multi-modal imitation learning from unstructured demonstrations using stochastic neural network modeling intention.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Bayesian Nonparametric Topic Model with Variational Auto-Encoders

Paper content:
Topic modeling of text documents is one of the most important tasks in representation learning.
In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders.
On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically.
On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.
Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically.
Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks.
Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.

assistant: text: This paper constructs an infinite Topic Model with Variational Auto-Encoders by combining Nalisnick & Smith's stick-breaking variational auto-encoder with latent Dirichlet allocation and several inference techniques used in Miao.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DeepSphere: a graph-based spherical CNN

Paper content:
Designing a convolution for a spherical neural network requires a delicate tradeoff between efficiency and rotation equivariance.
DeepSphere, a method based on a graph representation of the discretized sphere, strikes a controllable balance between these two desiderata.
This contribution is twofold.
First, we study both theoretically and empirically how equivariance is affected by the underlying graph with respect to the number of pixels and neighbors.
Second, we evaluate DeepSphere on relevant problems.
Experiments show state-of-the-art performance and demonstrates the efficiency and flexibility of this formulation.
Perhaps surprisingly, comparison with previous work suggests that anisotropic filters might be an unnecessary price to pay.

assistant: text: A graph-based spherical CNN that strikes an interesting balance of trade-offs for a wide variety of applications.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Paper content:
Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.
Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019).
However, surprisingly,  the simple baseline of just pre-training and fine-tuning compact models has been overlooked.
In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work.
Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation.
The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements.
Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data.
One surprising observation is that they have a compound effect even when sequentially applied on the same data.
To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.

assistant: text: Investigates training compact pre-trained language models via distillation and shows that using a teacher for distilling a compact student model performs better than directly pre-training the model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Extractor-Attention Network: A New Attention Network with Hybrid Encoders for Chinese Text Classification

Paper content:
Chinese text classification has received more and more attention today.
However, the problem of Chinese text representation still hinders the improvement of Chinese text classification, especially the polyphone and the homophone in social media.
To cope with it effectively, we propose a new structure, the Extractor, based on attention mechanisms and design novel attention networks named Extractor-attention network (EAN).
Unlike most of previous works, EAN uses a combination of a word encoder and a Pinyin character encoder instead of a single encoder.
It improves the capability of Chinese text representation.
Moreover, compared with the hybrid encoder methods, EAN has more complex combination architecture and more reducing parameters structures.
Thus, EAN can take advantage of a large amount of information that comes from multi-inputs and alleviates efficiency issues.
The proposed model achieves the state of the art results on 5 large datasets for Chinese text classification.

assistant: text: This paper proposes an attention-based model consisting of the word encoder and Pinyin encoder for the Chinese text classification task, and extends the architecture for the Pinyin character encoder.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS

Paper content:
This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.
Different from existing works, our PiT framework employs the sparse penalties to train networks and thus help rank the importance of weights and filters.
Our PiT algorithms can directly prune the network without any fine-tuning.
The pruned networks can still achieve comparable performance to the original networks.
In particular, we introduce the (Group) Lasso-type Penalty (L-P /GL-P), and (Group) Split LBI Penalty (S-P / GS-P) to regularize the networks, and a pruning strategy proposed  is used in help prune the network.
We conduct the extensive experiments on MNIST, Cifar-10, and miniImageNet.
The results validate the efficacy of our proposed methods.
Remarkably, on MNIST dataset, our PiT framework can save 17.5% parameter size of LeNet-5, which achieves the 98.47% recognition accuracy.

assistant: text: we propose an algorithm of learning to prune network by enforcing structure sparsity penalties
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Neural-Symbolic Approach to Natural Language Tasks

Paper content:
Deep learning (DL) has in recent years been widely used in natural
language processing (NLP) applications due to its superior
performance.
However, while natural languages are rich in
grammatical structure, DL has not been able to explicitly
represent and enforce such structures.
This paper proposes a new
architecture to bridge this gap by exploiting tensor product
representations (TPR), a structured neural-symbolic framework
developed in cognitive science over the past 20 years, with the
aim of integrating DL with explicit language structures and rules.
We call it the Tensor Product Generation Network
(TPGN), and apply it to image captioning.
The key
ideas of TPGN are:
1) unsupervised learning of
role-unbinding vectors of words via a TPR-based deep neural
network, and
2) integration of TPR with typical DL architectures
including Long Short-Term Memory (LSTM) models.
The novelty of our
approach lies in its ability to generate a sentence and extract
partial grammatical structure of the sentence by using
role-unbinding vectors, which are obtained in an unsupervised
manner.
Experimental results demonstrate the effectiveness of the
proposed approach.

assistant: text: This paper is intended to develop a tensor product representation approach for deep-learning-based natural language processinig applications.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CONFEDERATED MACHINE LEARNING ON HORIZONTALLY AND VERTICALLY SEPARATED MEDICAL DATA FOR LARGE-SCALE HEALTH SYSTEM INTELLIGENCE

Paper content:
A patient’s health information is generally fragmented across silos.
Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization.
Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care.
But federated learning cannot handle the situation where different data types for a given
patient are separated vertically across different organizations.
We call methods that enable machine learning model training on data separated by two or more degrees “confederated machine learning.”
We built and evaluated a confederated machine
learning
model to stratify the risk of accidental falls among the elderly.

assistant: text: a confederated learning method that train model from horizontally and vertically separated medical data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search

Paper content:
Differentiable architecture search (DARTS) provided a fast solution in finding effective network architectures, but suffered from large memory and computing overheads in jointly training a super-net and searching for an optimal architecture.
In this paper, we present a novel approach, namely  Partially-Connected DARTS, by sampling a small part of super-net to reduce the redundancy in exploring the network space, thereby performing a more efficient search without comprising the performance.
In particular, we perform operation search in a subset of channels while bypassing the held out part in a shortcut.
This strategy may suffer from an undesired inconsistency on selecting the edges of super-net caused by sampling different channels.
We solve it by introducing  edge normalization, which adds a new set of edge-level hyper-parameters to reduce uncertainty in search.
Thanks to the reduced memory cost, PC-DARTS can be trained with a larger batch size and, consequently, enjoy both faster speed and higher training stability.
Experiment results demonstrate the effectiveness of the proposed method.
Specifically, we achieve an error rate of 2.57% on CIFAR10 within merely 0.1 GPU-days for architecture search, and a state-of-the-art top-1 error rate of 24.2% on ImageNet (under the mobile setting) within 3.8 GPU-days for search.
Our code has been made available at https://www.dropbox.com/sh/on9lg3rpx1r6dkf/AABG5mt0sMHjnEJyoRnLEYW4a?dl=0
.

assistant: text: This paper proposes to improve DARTS in terms of training efficiency, from large memory and computing overheads, and proposes a partially-connected DARTS with partial channel connection and edge normalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: In-Domain Representation Learning For Remote Sensing

Paper content:
Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.
To address it and to speed up innovation in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form.
We specifically explore in-domain representation learning and address the question of "what characteristics should a dataset have to be a good source for remote sensing representation learning".
The established baselines achieve state-of-the-art performance on these datasets.

assistant: text: This paper provided several standardized remote sensing data sets and showed that in-domain representation could produce better baseline results for remote sensing compared to fine-tuning on ImageNet or learning from scratch.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods

Paper content:
The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning.
The Hessian is often used to understand these geometric properties.
We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian.
Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian.
We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16.
To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature.
This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

assistant: text: This paper analyzes the spectrum of the Hessian matrix of large neural networks, with an analysis of max/min eigenvalues and visualization of spectra using a Lanczos quadrature approach.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MultiGrain: a unified image embedding for classes and instances

Paper content:
We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition.
MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss.
The self-supervised loss only uses data augmentation and thus does not require additional labels.
Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy.
When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method.
The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution.
An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.

assistant: text: This paper proposes a unified embedding for image classification and instance retrieval to enhance the performance for both tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fully Quantized Transformer for Improved Translation

Paper content:
State-of-the-art neural machine translation methods employ massive amounts of parameters.
Drastically reducing computational costs of such methods without affecting performance has been up to this point unsolved.
In this work, we propose a quantization strategy tailored to the Transformer architecture.
We evaluate our method on the WMT14 EN-FR and WMT14 EN-DE translation tasks and achieve state-of-the-art quantization results for the Transformer, obtaining no loss in BLEU scores compared to the non-quantized baseline.
We further compress the Transformer by showing that, once the model is trained, a good portion of the nodes in the encoder can be removed without causing any loss in BLEU.

assistant: text: An 8-bit quantization method to quantize the machine translation model Transformer, proposing to use uniform min-max quantization during inference and bucketing weigts before quantization to reduce quantization error.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN

Paper content:
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.
Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint.
In this study, we prove the local stability of optimizing the simple gradient penalty $\mu$-WGAN(SGP $\mu$-WGAN) under suitable assumptions regarding the equilibrium and penalty measure $\mu$.
The measure valued differentiation concept is employed to deal with the derivative of the penalty terms, which is helpful for handling abstract singular measures with lower dimensional support.
Based on this analysis, we claim that penalizing the data manifold or sample manifold is the key to regularizing the original WGAN with a gradient penalty.
Experimental results obtained with unintuitive penalty measures that satisfy our assumptions are also provided to support our theoretical results.

assistant: text: WGAN with a squared zero centered gradient penalty term w.r.t. to a general measure is studied.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing

Paper content:
It is well-known that  classifiers are vulnerable to adversarial perturbations.
To defend against adversarial perturbations, various certified robustness results have been derived.
However, existing certified robustnesses are limited to top-1 predictions.
In many real-world applications, top-$k$ predictions are more relevant.
In this work, we aim to derive certified robustness for top-$k$ predictions.
In particular, our certified robustness is based on randomized smoothing, which turns any classifier to a new classifier via adding noise to an input example.
We adopt randomized smoothing because it is scalable to large-scale neural networks and applicable to any classifier.
We derive a tight robustness in $\ell_2$ norm for top-$k$ predictions  when using randomized smoothing with Gaussian noise.
We find that generalizing the certified robustness  from top-1 to top-$k$ predictions faces significant technical challenges.
We also empirically evaluate our method on CIFAR10 and ImageNet.
For example, our method can obtain an ImageNet classifier with a certified top-5 accuracy of 62.8\% when the $\ell_2$-norms of the adversarial perturbations are less than 0.5 (=127/255).
Our code is publicly available at: \url{https://github.com/jjy1994/Certify_Topk}.

assistant: text: We study the certified robustness for top-k predictions via randomized smoothing under Gaussian noise and derive a tight robustness bound in L_2 norm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adaptive Estimators Show Information Compression in Deep Neural Networks

Paper content:
To improve how neural networks function it is crucial to understand their learning process.
The information bottleneck theory of deep learning proposes that neural networks achieve good generalization by compressing their representations to disregard information that is not relevant to the task.
However, empirical evidence for this theory is conflicting, as compression was only observed when networks used saturating activation functions.
In contrast, networks with non-saturating activation functions achieved comparable levels of task performance but did not show compression.
In this paper we developed more robust mutual information estimation techniques, that adapt to hidden activity of neural networks and produce more sensitive measurements of activations from all functions, especially unbounded functions.
Using these adaptive estimation techniques, we explored compression in networks with a range of different activation functions.
With two improved methods of estimation, firstly, we show that saturation of the activation function is not required for compression, and the amount of compression varies between different activation functions.
We also find that there is a large amount of variation in compression between different network initializations.
Secondary, we see that L2 regularization leads to significantly increased compression, while preventing overfitting.
Finally, we show that only compression of the last layer is positively correlated with generalization.

assistant: text: We developed robust mutual information estimates for DNNs and used them to observe compression in networks with non-saturating activation functions
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: When is a Convolutional Filter Easy to Learn?

Paper content:
We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function.
Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input.
We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches.
To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions.
Our theory also justifies the two-stage learning rate strategy in deep neural networks.
While our focus is theoretical, we also present experiments that justify our theoretical findings.

assistant: text: Studies the problem of learning a single convolutional filter using SGD and shows that under certain conditions, SGD learns a single convolutional filter.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Match prediction from group comparison data using neural networks

Paper content:
We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.
Challenges arise in practice.
As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.
Worse yet, we have no prior knowledge on the underlying model for a given scenario.
These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances.
To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common.
This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand.
Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms.
It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets.
Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.

assistant: text: This paper provides a technique to solve the match prediction problem using a deep learning architecture.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions

Paper content:
The driving force behind deep networks is their ability to compactly represent rich classes of functions.
The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another.
To date, expressive efficiency analyses focused on the architectural feature of depth, showing that deep networks are representationally superior to shallow ones.
In this paper we study the expressive efficiency brought forth by connectivity, motivated by the observation that modern networks interconnect their layers in elaborate ways.
We focus on dilated convolutional networks, a family of deep models delivering state of the art performance in sequence processing tasks.
By introducing and analyzing the concept of mixed tensor decompositions, we prove that interconnecting dilated convolutional networks can lead to expressive efficiency.
In particular, we show that even a single connection between intermediate layers can already lead to an almost quadratic gap, which in large-scale settings typically makes the difference between a model that is practical and one that is not.
Empirical evaluation demonstrates how the expressive efficiency of connectivity, similarly to that of depth, translates into gains in accuracy.
This leads us to believe that expressive efficiency may serve a key role in developing new tools for deep network design.

assistant: text: We introduce the notion of mixed tensor decompositions, and use it to prove that interconnecting dilated convolutional networks boosts their expressive power.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

Paper content:
Computations for the softmax function in neural network models are expensive when the number of output classes is large.
This can become a significant issue in both training and inference for such models.
In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.
During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.
Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.
During inference, our method quickly locates the most probable expert to compute small-scale softmax.
Our method is learning-based and requires no knowledge of the output class partition space a priori.
We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of performance.

assistant: text: This paper proposes a sparse mixture of sparse experts that learns a two-level class hierarchy for efficient softmax inference.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Grouping-By-ID: Guarding Against Adversarial Domain Shifts

Paper content:
When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of
(i) “core” or “conditionally invariant” features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and
(ii) “style” or “orthogonal” features X^orth whose distribution P(X^orth | Y) can change substantially across domains.
These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons.
We try to guard against future adversarial domain shifts by ideally just using the “conditionally invariant” features for classification.
In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable.
We can hence not directly see the distributional change of features across different domains.
We do assume, however, that we can sometimes observe a so-called identifier or ID variable.
We might know, for example, that two images show the same person, with ID referring to the identity of the person.
In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image.
The method requires only a small fraction of images to have an ID variable.
We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016).
However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable.
If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.
Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian.
This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture.
We show links to questions of interpretability, fairness and transfer learning.

assistant: text: This paper aims at robust image classification against adversarial domain shifts and the goal is achieved by avoiding using the changing style features.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A PRIVACY-PRESERVING IMAGE CLASSIFICATION FRAMEWORK WITH A LEARNABLE OBFUSCATOR

Paper content:
Real world images often contain large amounts of private / sensitive information that should be carefully protected without reducing their utilities.
In this paper, we propose a privacy-preserving deep learning framework with a learnable ob- fuscator for the image classification task.
Our framework consists of three mod- els: learnable obfuscator, classifier and reconstructor.
The learnable obfuscator is used to remove the sensitive information in the images and extract the feature maps from them.
The reconstructor plays the role as an attacker, which tries to recover the image from the feature maps extracted by the obfuscator.
In order to best protect users’ privacy in images, we design an adversarial training methodol- ogy for our framework to optimize the obfuscator.
Through extensive evaluations on real world datasets, both the numerical metrics and the visualization results demonstrate that our framework is qualified to protect users’ privacy and achieve a relatively high accuracy on the image classification task.

assistant: text: We proposed a novel deep learning image classification framework that can both accurately classify images and protect users' privacy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Prestopping: How Does Early Stopping Help Generalization Against Label Noise?

Paper content:
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.
Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.
Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4–8.2 percent points under existence of real-world noise.

assistant: text: Paper proposes to study how early stopping in optimization helps find confident examples
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lifelong Generative Modeling

Paper content:
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning.
It is essential towards the development of intelligent machines that can adapt to their surroundings.
In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newly observed streaming distributions into our learnt model.
We do so through a student-teacher architecture which allows us to learn and preserve all the distributions seen so far without the need to retain the past data nor the past models.
Through the introduction of a novel cross-model regularizer, the student model leverages the information learnt by the teacher, which acts as a summary of everything seen till now.
The regularizer has the additional benefit of reducing the effect of catastrophic interference that appears when we learn over streaming data.
We demonstrate its efficacy on streaming distributions as well as its ability to learn a common latent representation across a complex transfer learning scenario.

assistant: text: Lifelong distributional learning through a student-teacher architecture coupled with a cross model posterior regularizer.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Min-max Entropy for Weakly Supervised Pointwise Localization

Paper content:
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.
In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.
Typical convolutional attentions maps are prune to high false positive regions.
To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.
Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.
A public reproducible PyTorch implementation is provided.

assistant: text: This paper presents a new weakly supervised approach for learning object segmentation with image-level class labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A novel text representation which enables image classifiers to perform text classification

Paper content:
We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems.
We apply the technique to entity disambiguation of inventor names in US patents.
The method involves converting text from each pairwise comparison between two inventor name records into a 2D RGB (stacked) image representation.
We then train an image classification neural network to discriminate between such pairwise comparison images, and use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors), obtaining highly accurate results (F1: 99.09%, precision: 99.41%, recall: 98.76%).
Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, such as disambiguation of academic publications, or for problems that require simultaneous classification of both text and images.

assistant: text: We introduce a novel text representation method which enables image classifiers to be applied to text classification problems, and apply the method to inventor name disambiguation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Outlier Rejection for Self-Supervised Keypoint Learning

Paper content:
Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms.
Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks.
However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators.
We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description.
We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution.
Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.

assistant: text: This paper is devoted to the self-supervised learning of local features using Neural Guided RANSAC as an additional auxillary loss provider for improving descriptor interpolation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Address2vec: Generating vector embeddings for blockchain analytics

Paper content:
Bitcoin is a virtual coinage system that enables users to trade virtually free of a central trusted authority.
All transactions on the Bitcoin blockchain are publicly available for viewing, yet as Bitcoin is built mainly for security it’s original structure does not allow for direct analysis of address transactions.
Existing analysis methods of the Bitcoin blockchain can be complicated, computationally expensive or inaccurate.
We propose a computationally efficient model to analyze bitcoin blockchain addresses and allow for their use with existing machine learning algorithms.
We compare our approach against Multi Level Sequence Learners (MLSLs), one of the best performing models on bitcoin address data.

assistant: text: a 2vec model for cryptocurrency transaction graphs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Challenges in Disentangling Independent Factors of Variation

Paper content:
We study the problem of building models that disentangle independent factors of variation.
Such models encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis.
As data we use a weakly labeled training set, where labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown.
This labeling is of particular interest as it may be readily available without annotation costs.
We introduce an autoencoder model and train it through constraints on image pairs and triplets.
We show the role of feature dimensionality and adversarial training theoretically and experimentally.
We formally prove the existence of the reference ambiguity, which is inherently present in the disentangling task when weakly labeled data is used.
The numerical value of a factor has different meaning in different reference frames.
When the reference depends on other factors, transferring that factor becomes ambiguous.
We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.

assistant: text: This paper considers disentangling factors of variation in images, shows that in general, without further assumptions, one cannot tell apart two different variation factors, and suggests a novel AE+GAN architecture to try and disentangle variation factors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Event Discovery for History Representation in Reinforcement Learning

Paper content:
Environments in Reinforcement Learning (RL) are usually only partially observable.
To address this problem, a possible solution is to provide the agent with information about past  observations.
While common methods represent this history using a Recurrent Neural Network (RNN), in this paper we propose an alternative representation which is based on the record of the past events observed in a given episode.
Inspired by the human memory, these events describe only important changes in the environment and, in our approach, are automatically discovered using self-supervision.
We evaluate our history representation method using two challenging RL benchmarks: some games of the Atari-57 suite and the 3D environment Obstacle Tower.
Using these benchmarks we show the advantage of our solution with respect to common RNN-based approaches.

assistant: text: This paper proposes a new way to represent past history as input to an RL agent, showing to perform better than PPO and an RNN variant of PPO.